From 0787883b32ea6df9a345453f6502471b38a6a2f1 Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Wed, 27 Aug 2025 15:43:29 +0200
Subject: [PATCH 1/7] User has to call reset explicitly

---
 docs/introduction/basic_usage.md           |   2 ++
 engibench/core.py                          |  19 ++++++++++++++++---
 engibench/problems/.DS_Store               | Bin 0 -> 6148 bytes
 engibench/problems/airfoil/v0.py           |   4 ++++
 engibench/problems/beams2d/v0.py           |   5 +++++
 engibench/problems/heatconduction2d/v0.py  |   4 ++++
 engibench/problems/heatconduction3d/v0.py  |   4 ++++
 engibench/problems/photonics2d/v0.py       |   4 ++++
 engibench/problems/power_electronics/v0.py |   2 ++
 engibench/problems/thermoelastic2d/v0.py   |   4 ++++
 tests/test_problem_implementations.py      |   1 +
 11 files changed, 46 insertions(+), 3 deletions(-)
 create mode 100644 engibench/problems/.DS_Store

diff --git a/docs/introduction/basic_usage.md b/docs/introduction/basic_usage.md
index 536bbeb9..984da29f 100644
--- a/docs/introduction/basic_usage.md
+++ b/docs/introduction/basic_usage.md
@@ -7,6 +7,7 @@ from engibench.problems.beams2d.v0 import Beams2D
 
 # Create a problem
 problem = Beams2D()
+problem.reset(seed=0)
 
 # Inspect problem
 problem.design_space  # Box(0.0, 1.0, (50, 100), float64)
@@ -33,6 +34,7 @@ violated_constraints = problem.check_constraints(generated_design, desired_conds
 if not violated_constraints:
     # Only simulate to get objective values
     objs = problem.simulate(design=generated_design, config=desired_conds)
+    problem.reset(seed=42)
     # Or run a gradient-based optimizer to polish the generate design
     opt_design, history = problem.optimize(starting_point=generated_design, config=desired_conds)
 ```
diff --git a/engibench/core.py b/engibench/core.py
index 9ae2ea50..2bb13893 100644
--- a/engibench/core.py
+++ b/engibench/core.py
@@ -4,7 +4,7 @@
 import dataclasses
 from enum import auto
 from enum import Enum
-from typing import Any, Generic, TypeVar
+from typing import Any, Callable, Generic, TypeVar
 
 from datasets import Dataset
 from datasets import load_dataset
@@ -45,7 +45,7 @@ class Problem(Generic[DesignType]):
     - :meth:`check_constraints` - to check if a design and conditions violate any constraints.
     - :meth:`simulate` - to simulate a design and return the performance given some conditions.
     - :meth:`optimize` - to optimize a design starting from a given point, e.g., using adjoint solver included inside the simulator.
-    - :meth:`reset` - to reset the simulator and numpy random to a given seed.
+    - :meth:`reset` - to reset the simulator and numpy random to a given seed. Should be called before each call to `simulate` or `optimize`.
     - :meth:`render` - to render a design in a human-readable format.
     - :meth:`random_design` - to generate a valid random design.
 
@@ -105,7 +105,7 @@ def __init__(self, **kwargs: Any) -> None:
         Args:
             **kwargs: Keyword arguments.
         """
-        self.reset(**kwargs)
+        self.reset_called = False
 
     @property
     def dataset(self) -> Dataset:
@@ -124,6 +124,18 @@ def conditions_keys(self) -> list[str]:
         """Returns the condition names as a list."""
         return [f.name for f in dataclasses.fields(self.conditions)]
 
+    def _check_reset_called(self, func_name: str, *, toggle: bool = True) -> None:
+        """Check if reset() has been called before calling func().
+
+        Args:
+            func_name (str): The name of the function to check.
+            toggle (bool): Whether to toggle the reset_called attribute.
+        """
+        if not self.reset_called:
+            raise RuntimeError(f"reset() must be called before each {func_name}()")
+        if toggle:
+            self.reset_called = False
+
     def simulate(self, design: DesignType, config: dict[str, Any] | None = None) -> npt.NDArray:
         r"""Launch a simulation on the given design and return the performance.
 
@@ -159,6 +171,7 @@ def reset(self, seed: int | None = None) -> None:
         Args:
             seed (int, optional): The seed to reset to. If None, a random seed is used.
         """
+        self.reset_called = True
         self.seed = seed
         self.np_random = np.random.default_rng(seed)
 
diff --git a/engibench/problems/.DS_Store b/engibench/problems/.DS_Store
new file mode 100644
index 0000000000000000000000000000000000000000..25c27827c31f547f65aad43f7ce989784931a1ed
GIT binary patch
literal 6148
zcmeHK%}T>S5T0$TO({YS3gRi?wP61c#Y?F51&ruHr6x_W!I&*=Y7V842hbPtL3|!(
zcDG8Y;zdNt49tEzvopKQw_!I60HQMu8vr%{Sg3@B3N~K|jgziO!FULTnxhXA5(r@m
z{gr5T{6_|8?`#;t0DMSs*FN_zg-BT~2!m`g3St>x>}xQJqe)t?zllPzw6R&X%2w67
z^@cL@Qa_zeI{x^YtP3gQVA1!3%V?1G?Cn#Tq<)kP#wsBS2N-g56(ym}I&zwX6P4@f
z0jpwFdiLII);w<2Yfhta)SlOz*>Mw%cI$9HuUI?#2Pfyfhxjp(&zfF=U!syNg9~`U
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z-$xoR5t3k^-V%h4LDyie5l2vjPDRwI!aOmAPDj6E;#`BdMx72qt&H=Sm4$hs2(>!;
z9Tg73)yOR~zzi%iP}HVP_y5`V@Bif_wwM8CV67MsrLNcQU`gg~T`7+4S_kzGm4xDQ
njUOr4QCBg>(p9{Rss#NGbr4;HxkmJ$@Q;9^fg5JvPZ@Xz?m|+`

literal 0
HcmV?d00001

diff --git a/engibench/problems/airfoil/v0.py b/engibench/problems/airfoil/v0.py
index 2857ea6c..24d88374 100644
--- a/engibench/problems/airfoil/v0.py
+++ b/engibench/problems/airfoil/v0.py
@@ -390,6 +390,8 @@ def simulate(self, design: DesignType, config: dict[str, Any] | None = None, mpi
         Returns:
             dict: The performance of the design - each entry of the dict corresponds to a named objective value.
         """
+        self._check_reset_called("simulate")
+
         if isinstance(design["angle_of_attack"], np.ndarray):
             design["angle_of_attack"] = design["angle_of_attack"][0]
 
@@ -458,6 +460,8 @@ def optimize(
         Returns:
             tuple[dict[str, Any], list[OptiStep]]: The optimized design and its performance.
         """
+        self._check_reset_called("optimize")
+
         if isinstance(starting_point["angle_of_attack"], np.ndarray):
             starting_point["angle_of_attack"] = starting_point["angle_of_attack"][0]
 
diff --git a/engibench/problems/beams2d/v0.py b/engibench/problems/beams2d/v0.py
index a5f9f4d7..2dbc51c2 100644
--- a/engibench/problems/beams2d/v0.py
+++ b/engibench/problems/beams2d/v0.py
@@ -193,6 +193,8 @@ def simulate(
         Returns:
             npt.NDArray: The performance of the design in terms of compliance.
         """
+        self._check_reset_called("simulate")
+
         # This condition is needed to convert user-provided designs (images) to flat arrays. Normally does not apply, i.e., during optimization.
         if len(design.shape) > 1:
             design = image_to_design(design)
@@ -225,6 +227,8 @@ def optimize(
         Returns:
             Tuple[np.ndarray, dict]: The optimized design and its performance.
         """
+        self._check_reset_called("optimize")
+
         base_config = self.Config(**{**dataclasses.asdict(self.simulate_config), **(config or {})})
 
         self.__st = State.new(base_config.nelx, base_config.nely, base_config.rmin, base_config.forcedist)
@@ -252,6 +256,7 @@ def optimize(
         while change > self.__st.min_change and loop < base_config.max_iter:
             ce = calc_sensitivity(xPrint, st=self.__st, cfg=dataclasses.asdict(base_config))
             simulate_config = upcast(base_config)
+            self.reset_called = True  # override for multiple reset calls in optimize
             c = self.simulate(xPrint, ce=ce, config=dataclasses.asdict(simulate_config))
 
             # Record the current state in optisteps_history
diff --git a/engibench/problems/heatconduction2d/v0.py b/engibench/problems/heatconduction2d/v0.py
index 8ace8677..1114acf7 100644
--- a/engibench/problems/heatconduction2d/v0.py
+++ b/engibench/problems/heatconduction2d/v0.py
@@ -131,6 +131,8 @@ def simulate(self, design: npt.NDArray | None = None, config: dict[str, Any] | N
         Returns:
             float: The thermal compliance of the design.
         """
+        self._check_reset_called("simulate")
+
         config = config or {}
         volume = config.get("volume", self.config.volume)
         length = config.get("length", self.config.length)
@@ -169,6 +171,8 @@ def optimize(
         Returns:
             Tuple[OptimalDesign, list[OptiStep]]: The optimized design and the optimization history.
         """
+        self._check_reset_called("optimize")
+
         config = config or {}
         volume = config.get("volume", self.config.volume)
         length = config.get("length", self.config.length)
diff --git a/engibench/problems/heatconduction3d/v0.py b/engibench/problems/heatconduction3d/v0.py
index 2b7283c6..fca108c4 100644
--- a/engibench/problems/heatconduction3d/v0.py
+++ b/engibench/problems/heatconduction3d/v0.py
@@ -131,6 +131,8 @@ def simulate(self, design: npt.NDArray | None = None, config: dict[str, Any] | N
         Returns:
             float: The thermal compliance of the design.
         """
+        self._check_reset_called("simulate")
+
         config = config or {}
         volume = config.get("volume", self.config.volume)
         area = config.get("area", self.config.area)
@@ -169,6 +171,8 @@ def optimize(
         Returns:
             Tuple[OptimalDesign, list[OptiStep]]: The optimized design and the optimization history.
         """
+        self._check_reset_called("optimize")
+
         config = config or {}
         volume = config.get("volume", self.config.volume)
         area = config.get("area", self.config.area)
diff --git a/engibench/problems/photonics2d/v0.py b/engibench/problems/photonics2d/v0.py
index d4ba64fd..4988bb30 100644
--- a/engibench/problems/photonics2d/v0.py
+++ b/engibench/problems/photonics2d/v0.py
@@ -338,6 +338,8 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None, **
         Returns:
             npt.NDArray: 1-element array: [total_overlap - penalty], where higher is better.
         """
+        self._check_reset_called("simulate")
+
         conditions = self._setup_simulation(config)
 
         # --- Run Simulation ---
@@ -385,6 +387,8 @@ def optimize(  # noqa: PLR0915
                   value of the internally optimized objective (i.e., `total_overlap - penalty`).
                   The `step` attribute corresponds to the optimizer iteration.
         """
+        self._check_reset_called("optimize")
+
         conditions = self._setup_simulation(config)
 
         # Reset the current beta to one for the optimization
diff --git a/engibench/problems/power_electronics/v0.py b/engibench/problems/power_electronics/v0.py
index 01fbc5e3..50065806 100644
--- a/engibench/problems/power_electronics/v0.py
+++ b/engibench/problems/power_electronics/v0.py
@@ -153,6 +153,8 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None) ->
         Returns:
             simulation_results: a numpy array containing the simulation results [DcGain, VoltageRipple, Efficiency].
         """
+        self._check_reset_called("simulate")
+
         self.config, rewrite_netlist_str, edge_map, _ = parse_topology(self.config)
         self.config = process_sweep_data(config=self.config, sweep_data=design.tolist())
         rewrite_netlist(self.config, rewrite_netlist_str, edge_map)
diff --git a/engibench/problems/thermoelastic2d/v0.py b/engibench/problems/thermoelastic2d/v0.py
index e94a16a9..19de84f7 100644
--- a/engibench/problems/thermoelastic2d/v0.py
+++ b/engibench/problems/thermoelastic2d/v0.py
@@ -164,6 +164,8 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None) ->
         Returns:
             dict: The performance of the design - each entry of the dict corresponds to a named objective value.
         """
+        self._check_reset_called("simulate")
+
         boundary_dict = dataclasses.asdict(self.conditions)
         for key, value in (config or {}).items():
             if key in boundary_dict:
@@ -187,6 +189,8 @@ def optimize(
         Returns:
             Tuple[np.ndarray, dict]: The optimized design and its performance.
         """
+        self._check_reset_called("optimize")
+
         boundary_dict = dataclasses.asdict(self.conditions)
         boundary_dict.update({k: v for k, v in (config or {}).items() if k in boundary_dict})
         results = FeaModel(plot=False, eval_only=False).run(boundary_dict, x_init=starting_point)
diff --git a/tests/test_problem_implementations.py b/tests/test_problem_implementations.py
index 9d405d9a..d7ff868e 100644
--- a/tests/test_problem_implementations.py
+++ b/tests/test_problem_implementations.py
@@ -116,6 +116,7 @@ def test_python_problem_impl(problem_class: type[Problem]) -> None:
     if problem_class.__module__.startswith("engibench.problems.power_electronics"):
         print(f"Skipping optimization test for power electronics problem {problem_class.__name__}")
         return
+    problem.reset(seed=1)
     optimal_design, history = problem.optimize(starting_point=design)
     if isinstance(problem.design_space, spaces.Box):
         assert np.all(optimal_design >= problem.design_space.low), (

From 7429b99ecdcd642ebcb4a7ae6ed407c34c7c5e5e Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Wed, 27 Aug 2025 15:45:56 +0200
Subject: [PATCH 2/7] ruff

---
 engibench/core.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/engibench/core.py b/engibench/core.py
index 2bb13893..7e5e14df 100644
--- a/engibench/core.py
+++ b/engibench/core.py
@@ -4,7 +4,7 @@
 import dataclasses
 from enum import auto
 from enum import Enum
-from typing import Any, Callable, Generic, TypeVar
+from typing import Any, Generic, TypeVar
 
 from datasets import Dataset
 from datasets import load_dataset

From 9fd1bcf51c7de6b458abac5a0d74b4a534e8281f Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Wed, 27 Aug 2025 15:50:30 +0200
Subject: [PATCH 3/7] Ruff

---
 engibench/core.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/engibench/core.py b/engibench/core.py
index 7e5e14df..fa7347b7 100644
--- a/engibench/core.py
+++ b/engibench/core.py
@@ -99,12 +99,8 @@ class Problem(Generic[DesignType]):
     # This handles the RNG properly
     np_random: np.random.Generator
 
-    def __init__(self, **kwargs: Any) -> None:
-        """Initialize the problem.
-
-        Args:
-            **kwargs: Keyword arguments.
-        """
+    def __init__(self) -> None:
+        """Initialize the problem."""
         self.reset_called = False
 
     @property

From 1f431e30fcb52f6c13eb8b96d03b28e65fef31b2 Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Wed, 27 Aug 2025 15:51:33 +0200
Subject: [PATCH 4/7] Remove DS Store

---
 engibench/problems/.DS_Store | Bin 6148 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 engibench/problems/.DS_Store

diff --git a/engibench/problems/.DS_Store b/engibench/problems/.DS_Store
deleted file mode 100644
index 25c27827c31f547f65aad43f7ce989784931a1ed..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 6148
zcmeHK%}T>S5T0$TO({YS3gRi?wP61c#Y?F51&ruHr6x_W!I&*=Y7V842hbPtL3|!(
zcDG8Y;zdNt49tEzvopKQw_!I60HQMu8vr%{Sg3@B3N~K|jgziO!FULTnxhXA5(r@m
z{gr5T{6_|8?`#;t0DMSs*FN_zg-BT~2!m`g3St>x>}xQJqe)t?zllPzw6R&X%2w67
z^@cL@Qa_zeI{x^YtP3gQVA1!3%V?1G?Cn#Tq<)kP#wsBS2N-g56(ym}I&zwX6P4@f
z0jpwFdiLII);w<2Yfhta)SlOz*>Mw%cI$9HuUI?#2Pfyfhxjp(&zfF=U!syNg9~`U
z&X1KodE+FO$vws{=2_%3G6T#2Gq64km=n&bu1`r^2{XV9{1O9nJ~*g^uEAWRIy$hS
z-$xoR5t3k^-V%h4LDyie5l2vjPDRwI!aOmAPDj6E;#`BdMx72qt&H=Sm4$hs2(>!;
z9Tg73)yOR~zzi%iP}HVP_y5`V@Bif_wwM8CV67MsrLNcQU`gg~T`7+4S_kzGm4xDQ
njUOr4QCBg>(p9{Rss#NGbr4;HxkmJ$@Q;9^fg5JvPZ@Xz?m|+`


From e40098dd1e890277965884f79993a7ad4a6d2c65 Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Tue, 2 Sep 2025 09:12:53 +0200
Subject: [PATCH 5/7] API 4

---
 README.md                                  |  3 +--
 docs/introduction/basic_usage.md           |  3 +--
 engibench/core.py                          | 17 ++---------------
 engibench/problems/airfoil/v0.py           | 19 ++++++++-----------
 engibench/problems/beams2d/v0.py           | 13 +++++--------
 engibench/problems/heatconduction2d/v0.py  | 12 ++++--------
 engibench/problems/heatconduction3d/v0.py  | 11 ++++-------
 engibench/problems/photonics2d/v0.py       | 13 ++++++-------
 engibench/problems/power_electronics/v0.py | 11 ++++++-----
 engibench/problems/thermoelastic2d/v0.py   | 15 +++++----------
 tests/test_problem_implementations.py      |  3 +--
 11 files changed, 43 insertions(+), 77 deletions(-)

diff --git a/README.md b/README.md
index 704da360..108c107f 100644
--- a/README.md
+++ b/README.md
@@ -45,8 +45,7 @@ pip install "engibench[all]"
 from engibench.problems.beams2d.v0 import Beams2D
 
 # Create a problem
-problem = Beams2D()
-problem.reset(seed=0)
+problem = Beams2D(seed=0)
 
 # Inspect problem
 problem.design_space  # Box(0.0, 1.0, (50, 100), float64)
diff --git a/docs/introduction/basic_usage.md b/docs/introduction/basic_usage.md
index 984da29f..f914343d 100644
--- a/docs/introduction/basic_usage.md
+++ b/docs/introduction/basic_usage.md
@@ -6,8 +6,7 @@ Our API is designed to be simple and easy to use. Here is a basic example of how
 from engibench.problems.beams2d.v0 import Beams2D
 
 # Create a problem
-problem = Beams2D()
-problem.reset(seed=0)
+problem = Beams2D(seed=0)
 
 # Inspect problem
 problem.design_space  # Box(0.0, 1.0, (50, 100), float64)
diff --git a/engibench/core.py b/engibench/core.py
index fa7347b7..d4164079 100644
--- a/engibench/core.py
+++ b/engibench/core.py
@@ -99,9 +99,9 @@ class Problem(Generic[DesignType]):
     # This handles the RNG properly
     np_random: np.random.Generator
 
-    def __init__(self) -> None:
+    def __init__(self, seed: int = 0) -> None:
         """Initialize the problem."""
-        self.reset_called = False
+        self.reset(seed=seed)
 
     @property
     def dataset(self) -> Dataset:
@@ -120,18 +120,6 @@ def conditions_keys(self) -> list[str]:
         """Returns the condition names as a list."""
         return [f.name for f in dataclasses.fields(self.conditions)]
 
-    def _check_reset_called(self, func_name: str, *, toggle: bool = True) -> None:
-        """Check if reset() has been called before calling func().
-
-        Args:
-            func_name (str): The name of the function to check.
-            toggle (bool): Whether to toggle the reset_called attribute.
-        """
-        if not self.reset_called:
-            raise RuntimeError(f"reset() must be called before each {func_name}()")
-        if toggle:
-            self.reset_called = False
-
     def simulate(self, design: DesignType, config: dict[str, Any] | None = None) -> npt.NDArray:
         r"""Launch a simulation on the given design and return the performance.
 
@@ -167,7 +155,6 @@ def reset(self, seed: int | None = None) -> None:
         Args:
             seed (int, optional): The seed to reset to. If None, a random seed is used.
         """
-        self.reset_called = True
         self.seed = seed
         self.np_random = np.random.default_rng(seed)
 
diff --git a/engibench/problems/airfoil/v0.py b/engibench/problems/airfoil/v0.py
index 24d88374..c7ad3764 100644
--- a/engibench/problems/airfoil/v0.py
+++ b/engibench/problems/airfoil/v0.py
@@ -199,10 +199,11 @@ def area_ratio_bound(area_ratio_min: float, area_initial: float | None, area_inp
                 f"Config.area_ratio: {area_ratio} ∉ [area_ratio_min={area_ratio_min}, 1.2]"
             )
 
-    def __init__(self, base_directory: str | None = None) -> None:
+    def __init__(self, seed: int = 0, base_directory: str | None = None) -> None:
         """Initializes the Airfoil problem.
 
         Args:
+            seed (int): The random seed for the problem.
             base_directory (str, optional): The base directory for the problem. If None, the current directory is selected.
         """
         # This is used for intermediate files
@@ -222,7 +223,7 @@ def __init__(self, base_directory: str | None = None) -> None:
         self.__docker_base_dir = "/home/mdolabuser/mount/engibench"
         self.__docker_target_dir = self.__docker_base_dir + "/engibench_studies/problems/airfoil"
 
-        super().__init__()
+        super().__init__(seed=seed)
 
     def reset(self, seed: int | None = None, *, cleanup: bool = False) -> None:
         """Resets the simulator and numpy random to a given seed.
@@ -239,8 +240,6 @@ def reset(self, seed: int | None = None, *, cleanup: bool = False) -> None:
         self.__local_study_dir = self.__local_target_dir + "/" + self.current_study
         self.__docker_study_dir = self.__docker_target_dir + "/" + self.current_study
 
-        clone_dir(source_dir=self.__local_template_dir, target_dir=self.__local_study_dir)
-
     def __design_to_simulator_input(self, design: DesignType, config: dict[str, Any], filename: str = "design") -> str:
         """Converts a design to a simulator input.
 
@@ -252,6 +251,9 @@ def __design_to_simulator_input(self, design: DesignType, config: dict[str, Any]
             config (dict): A dictionary with configuration (e.g., boundary conditions) for the simulation.
             filename (str): The filename to save the design to.
         """
+        # Creates the study directory
+        clone_dir(source_dir=self.__local_template_dir, target_dir=self.__local_study_dir)
+
         tmp = os.path.join(self.__docker_study_dir, "tmp")
 
         # Calculate the off-the-wall distance
@@ -390,8 +392,6 @@ def simulate(self, design: DesignType, config: dict[str, Any] | None = None, mpi
         Returns:
             dict: The performance of the design - each entry of the dict corresponds to a named objective value.
         """
-        self._check_reset_called("simulate")
-
         if isinstance(design["angle_of_attack"], np.ndarray):
             design["angle_of_attack"] = design["angle_of_attack"][0]
 
@@ -460,8 +460,6 @@ def optimize(
         Returns:
             tuple[dict[str, Any], list[OptiStep]]: The optimized design and its performance.
         """
-        self._check_reset_called("optimize")
-
         if isinstance(starting_point["angle_of_attack"], np.ndarray):
             starting_point["angle_of_attack"] = starting_point["angle_of_attack"][0]
 
@@ -614,8 +612,7 @@ def random_design(self, dataset_split: str = "train", design_key: str = "initial
 if __name__ == "__main__":
     # Initialize the problem
 
-    problem = Airfoil()
-    problem.reset(seed=0, cleanup=True)
+    problem = Airfoil(seed=0)
 
     # Retrieve the dataset
     dataset = problem.dataset
@@ -630,7 +627,7 @@ def random_design(self, dataset_split: str = "train", design_key: str = "initial
     print(problem.simulate(design, config=config, mpicores=8))
 
     # Cleanup the study directory; will delete the previous contents from simulate in this case
-    problem.reset(seed=0, cleanup=False)
+    problem.reset(seed=0, cleanup=True)
 
     # Get design and conditions from the dataset, render design
     opt_design, optisteps_history = problem.optimize(design, config=config, mpicores=8)
diff --git a/engibench/problems/beams2d/v0.py b/engibench/problems/beams2d/v0.py
index 2dbc51c2..5e2b47db 100644
--- a/engibench/problems/beams2d/v0.py
+++ b/engibench/problems/beams2d/v0.py
@@ -159,13 +159,14 @@ def rmin_bound(rmin: float, nelx: int, nely: int) -> None:
     dataset_id = f"IDEALLab/beams_2d_{Config.nely}_{Config.nelx}_v{version}"
     container_id = None
 
-    def __init__(self, config: dict[str, Any] | None = None):
+    def __init__(self, seed: int = 0, config: dict[str, Any] | None = None):
         """Initializes the Beams2D problem.
 
         Args:
+            seed (int): The random seed for the problem.
             config (dict): A dictionary with configuration (e.g., boundary conditions) for the simulation.
         """
-        super().__init__()
+        super().__init__(seed=seed)
 
         # Replace the config with any new configs passed in
         self.config = self.Config(**(config or {}))
@@ -193,8 +194,6 @@ def simulate(
         Returns:
             npt.NDArray: The performance of the design in terms of compliance.
         """
-        self._check_reset_called("simulate")
-
         # This condition is needed to convert user-provided designs (images) to flat arrays. Normally does not apply, i.e., during optimization.
         if len(design.shape) > 1:
             design = image_to_design(design)
@@ -227,8 +226,6 @@ def optimize(
         Returns:
             Tuple[np.ndarray, dict]: The optimized design and its performance.
         """
-        self._check_reset_called("optimize")
-
         base_config = self.Config(**{**dataclasses.asdict(self.simulate_config), **(config or {})})
 
         self.__st = State.new(base_config.nelx, base_config.nely, base_config.rmin, base_config.forcedist)
@@ -336,8 +333,7 @@ def random_design(self, dataset_split: str = "train", design_key: str = "optimal
     # Possible sets of nely and nelx: (25, 50), (50, 100), and (100, 200)
     # If a new nely and nelx are not passed in, uses the default conditions.
 
-    problem = Beams2D()
-    problem.reset(seed=0)
+    problem = Beams2D(seed=0)
 
     print(f"Loading dataset for nely={problem.nely}, nelx={problem.nelx}.")
     dataset = problem.dataset
@@ -365,6 +361,7 @@ def random_design(self, dataset_split: str = "train", design_key: str = "optimal
 
     # Sample Optimization
     print("\nNow conducting a sample optimization with the given configs:", config)
+    problem.reset(seed=1)
 
     # NOTE: optimal_design and optisteps_history[-1].stored_design are interchangeable.
     optimal_design, optisteps_history = problem.optimize(config=config)
diff --git a/engibench/problems/heatconduction2d/v0.py b/engibench/problems/heatconduction2d/v0.py
index 1114acf7..dcb798b5 100644
--- a/engibench/problems/heatconduction2d/v0.py
+++ b/engibench/problems/heatconduction2d/v0.py
@@ -109,13 +109,14 @@ class Config(Conditions):
     dataset_id = "IDEALLab/heat_conduction_2d_v0"
     container_id = "quay.io/dolfinadjoint/pyadjoint:master"
 
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(self, seed: int = 0, **kwargs: Any) -> None:
         """Initialize the HeatConduction2D problem.
 
         Args:
+            seed (int): The random seed for the problem.
             kwargs: Arguments are passed to :class:`HeatConduction2D.Config`.
         """
-        super().__init__()
+        super().__init__(seed=seed)
         self.config = self.Config(**kwargs)
         resolution = self.config.resolution
         self.conditions = self.Conditions(self.config.volume, self.config.length)
@@ -131,8 +132,6 @@ def simulate(self, design: npt.NDArray | None = None, config: dict[str, Any] | N
         Returns:
             float: The thermal compliance of the design.
         """
-        self._check_reset_called("simulate")
-
         config = config or {}
         volume = config.get("volume", self.config.volume)
         length = config.get("length", self.config.length)
@@ -171,8 +170,6 @@ def optimize(
         Returns:
             Tuple[OptimalDesign, list[OptiStep]]: The optimized design and the optimization history.
         """
-        self._check_reset_called("optimize")
-
         config = config or {}
         volume = config.get("volume", self.config.volume)
         length = config.get("length", self.config.length)
@@ -287,8 +284,7 @@ def render(self, design: npt.NDArray, *, open_window: bool = False) -> Any:
 # Check if the script is run directly
 if __name__ == "__main__":
     # Create a HeatConduction2D problem instance
-    problem = HeatConduction2D()
-    problem.reset(seed=0)
+    problem = HeatConduction2D(seed=0)
     design_as_list = problem.dataset["train"]["optimal_design"][0]
     design_as_array = np.array(design_as_list)
     des, traj = problem.optimize(starting_point=design_as_array)
diff --git a/engibench/problems/heatconduction3d/v0.py b/engibench/problems/heatconduction3d/v0.py
index fca108c4..0bcd0966 100644
--- a/engibench/problems/heatconduction3d/v0.py
+++ b/engibench/problems/heatconduction3d/v0.py
@@ -109,13 +109,14 @@ class Config(Conditions):
     dataset_id = "IDEALLab/heat_conduction_3d_v0"
     container_id = "quay.io/dolfinadjoint/pyadjoint:master"
 
-    def __init__(self, **kwargs) -> None:
+    def __init__(self, seed: int = 0, **kwargs) -> None:
         """Initialize the HeatConduction3D problem.
 
         Args:
+            seed (int): The random seed for the problem.
             kwargs: Arguments are passed to :class:`HeatConduction3D.Config`.
         """
-        super().__init__()
+        super().__init__(seed=seed)
         self.config = self.Config(**kwargs)
         resolution = self.config.resolution
         self.conditions = self.Conditions(self.config.volume, self.config.area)
@@ -131,8 +132,6 @@ def simulate(self, design: npt.NDArray | None = None, config: dict[str, Any] | N
         Returns:
             float: The thermal compliance of the design.
         """
-        self._check_reset_called("simulate")
-
         config = config or {}
         volume = config.get("volume", self.config.volume)
         area = config.get("area", self.config.area)
@@ -171,8 +170,6 @@ def optimize(
         Returns:
             Tuple[OptimalDesign, list[OptiStep]]: The optimized design and the optimization history.
         """
-        self._check_reset_called("optimize")
-
         config = config or {}
         volume = config.get("volume", self.config.volume)
         area = config.get("area", self.config.area)
@@ -313,7 +310,7 @@ def render(self, design: npt.NDArray, *, open_window: bool = False) -> Any:
 # Check if the script is run directly
 if __name__ == "__main__":
     # Create a HeatConduction3D problem instance
-    problem = HeatConduction3D()
+    problem = HeatConduction3D(seed=0)
     design_as_list = problem.dataset["train"]["optimal_design"][0]
     numpy_array = np.array(design_as_list)
     des, traj = problem.optimize(starting_point=numpy_array)
diff --git a/engibench/problems/photonics2d/v0.py b/engibench/problems/photonics2d/v0.py
index 4988bb30..374fa50a 100644
--- a/engibench/problems/photonics2d/v0.py
+++ b/engibench/problems/photonics2d/v0.py
@@ -231,6 +231,7 @@ class Config(Conditions):
 
     def __init__(
         self,
+        seed: int = 0,
         config: dict[str, Any] | None = None,
         num_elems_x: int = Config.num_elems_x,
         num_elems_y: int = Config.num_elems_y,
@@ -239,12 +240,13 @@ def __init__(
         """Initializes the Photonics2D problem.
 
         Args:
+            seed (int): The random seed for the problem.
             config (dict): A dictionary with configuration (e.g., boundary conditions) for the simulation.
             num_elems_x (int): Number of grid cells in x (default: 120).
             num_elems_y (int): Number of grid cells in y (default: 120).
             **kwargs: Additional keyword arguments.
         """
-        super().__init__(**kwargs)
+        super().__init__(seed=seed, **kwargs)
 
         # Replace the conditions with any new configs passed in
         self.config = self.Config(num_elems_x=num_elems_x, num_elems_y=num_elems_y, **(config or {}))
@@ -338,8 +340,6 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None, **
         Returns:
             npt.NDArray: 1-element array: [total_overlap - penalty], where higher is better.
         """
-        self._check_reset_called("simulate")
-
         conditions = self._setup_simulation(config)
 
         # --- Run Simulation ---
@@ -387,8 +387,6 @@ def optimize(  # noqa: PLR0915
                   value of the internally optimized objective (i.e., `total_overlap - penalty`).
                   The `step` attribute corresponds to the optimizer iteration.
         """
-        self._check_reset_called("optimize")
-
         conditions = self._setup_simulation(config)
 
         # Reset the current beta to one for the optimization
@@ -708,8 +706,7 @@ def reset(self, seed: int | None = None, **kwargs) -> None:
         "lambda2": 0.84,
         "blur_radius": 1,
     }
-    problem = Photonics2D(config=problem_config, num_elems_x=120, num_elems_y=120)
-    problem.reset(seed=42)  # Use a seed
+    problem = Photonics2D(config=problem_config, num_elems_x=120, num_elems_y=120, seed=42)
 
     start_design, _ = problem.random_design(noise=0.1, blur=1)  # Randomized design with noise
     fig_start = problem.render(start_design)
@@ -723,6 +720,7 @@ def reset(self, seed: int | None = None, **kwargs) -> None:
     # Optimization Example
     # Advanced Usage: Modifying optimization parameters
     opt_config = {"num_optimization_steps": 200, "save_frame_interval": 2, "initial_beta": 1.0}
+    problem.reset(seed=42)
     print(f"Optimizing design with ({opt_config})...")
     # Optimize maximizes (overlap - penalty)
     optimized_design, opti_history = problem.optimize(start_design, config=opt_config)
@@ -738,6 +736,7 @@ def reset(self, seed: int | None = None, **kwargs) -> None:
 
     print("Simulating the final optimized design...")
     # Simulate returns the raw objective = penalty - overlap1*overlap2
+    problem.reset(seed=43)
     obj_opt_raw = problem.simulate(optimized_design)
     print(f"Optimized Raw Objective ({problem.objectives_keys[0]}): {obj_opt_raw[0]:.4f}")
 
diff --git a/engibench/problems/power_electronics/v0.py b/engibench/problems/power_electronics/v0.py
index 50065806..fb31655a 100644
--- a/engibench/problems/power_electronics/v0.py
+++ b/engibench/problems/power_electronics/v0.py
@@ -111,6 +111,7 @@ class Conditions:
 
     def __init__(
         self,
+        seed: int = 0,
         target_dir: str = os.getcwd(),
         original_netlist_path: str = os.path.join(
             os.path.dirname(os.path.abspath(__file__)), "data/5_4_3_6_10-dcdc_converter_1.net"
@@ -121,13 +122,14 @@ def __init__(
         """Initializes the Power Electronics problem.
 
         Args:
+            seed (int): The random seed for the problem.
             target_dir: The target directory for the rewritten netlist, log and raw files. Default to os.getcwd().
             original_netlist_path: The path to the original netlist file. Accepts both relative and absolute paths.
             bucket_id: The bucket ID for the netlist file. E.g. "5_4_3_6_10".
             mode: The mode for the simulation. Default to "control". mode = "batch" is for development.
             ngspice_path: The path to the ngspice executable for Windows.
         """
-        super().__init__()
+        super().__init__(seed=seed)
 
         self.config = Config(
             target_dir=target_dir,
@@ -153,8 +155,6 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None) ->
         Returns:
             simulation_results: a numpy array containing the simulation results [DcGain, VoltageRipple, Efficiency].
         """
-        self._check_reset_called("simulate")
-
         self.config, rewrite_netlist_str, edge_map, _ = parse_topology(self.config)
         self.config = process_sweep_data(config=self.config, sweep_data=design.tolist())
         rewrite_netlist(self.config, rewrite_netlist_str, edge_map)
@@ -206,10 +206,10 @@ def reset(self, seed: int | None = None) -> None:
 
 if __name__ == "__main__":
     # Test with absolute path and a different bucket_id
-    problem = PowerElectronics(mode="batch")
+    problem = PowerElectronics(seed=0, mode="batch")
 
     # Initialize the problem with default values
-    problem = PowerElectronics()
+    problem = PowerElectronics(seed=0)
 
     # Manually add the sweep data
     sweep_data = [
@@ -264,6 +264,7 @@ def reset(self, seed: int | None = None) -> None:
     ]
 
     # Simulate the problem with the provided design variable
+    problem.reset(seed=0)
     simulation_results = problem.simulate(design=np.array(sweep_data))
     print(simulation_results)  # [-1.27858   -0.025081   0.7827396]
 
diff --git a/engibench/problems/thermoelastic2d/v0.py b/engibench/problems/thermoelastic2d/v0.py
index 19de84f7..14084ab1 100644
--- a/engibench/problems/thermoelastic2d/v0.py
+++ b/engibench/problems/thermoelastic2d/v0.py
@@ -137,14 +137,13 @@ def rmin_bound(rmin: float, nelx: int, nely: int) -> None:
             """Constraint for rmin ∈ (0.0, max{ nelx, nely }]."""
             assert 0.0 < rmin <= max(nelx, nely), f"Params.rmin: {rmin} ∉ (0, max(nelx, nely)]"
 
-    def __init__(self) -> None:
+    def __init__(self, seed: int = 0) -> None:
         """Initializes the thermoelastic2D problem.
 
         Args:
-            base_directory (str, optional): The base directory for the problem. If None, the current directory is selected.
+            seed (int): The random seed for the problem.
         """
-        super().__init__()
-        self.seed = None
+        super().__init__(seed=seed)
 
     def reset(self, seed: int | None = None) -> None:
         """Resets the simulator and numpy random to a given seed.
@@ -164,8 +163,6 @@ def simulate(self, design: npt.NDArray, config: dict[str, Any] | None = None) ->
         Returns:
             dict: The performance of the design - each entry of the dict corresponds to a named objective value.
         """
-        self._check_reset_called("simulate")
-
         boundary_dict = dataclasses.asdict(self.conditions)
         for key, value in (config or {}).items():
             if key in boundary_dict:
@@ -189,8 +186,6 @@ def optimize(
         Returns:
             Tuple[np.ndarray, dict]: The optimized design and its performance.
         """
-        self._check_reset_called("optimize")
-
         boundary_dict = dataclasses.asdict(self.conditions)
         boundary_dict.update({k: v for k, v in (config or {}).items() if k in boundary_dict})
         results = FeaModel(plot=False, eval_only=False).run(boundary_dict, x_init=starting_point)
@@ -231,8 +226,7 @@ def random_design(self) -> tuple[npt.NDArray, int]:
 
 if __name__ == "__main__":
     # --- Create a new problem
-    problem = ThermoElastic2D()
-    problem.reset()
+    problem = ThermoElastic2D(seed=0)
 
     # --- Load the problem dataset
     dataset = problem.dataset
@@ -250,5 +244,6 @@ def random_design(self) -> tuple[npt.NDArray, int]:
     problem.render(design, open_window=True)
 
     # --- Evaluate a design ---
+    problem.reset(seed=0)
     design, _ = problem.random_design()
     print(problem.simulate(design))
diff --git a/tests/test_problem_implementations.py b/tests/test_problem_implementations.py
index d7ff868e..f805ed6e 100644
--- a/tests/test_problem_implementations.py
+++ b/tests/test_problem_implementations.py
@@ -97,8 +97,7 @@ def test_python_problem_impl(problem_class: type[Problem]) -> None:
     """
     print(f"Testing optimization and simulation for {problem_class.__name__}...")
     # Initialize problem and get a random design
-    problem = problem_class()
-    problem.reset(seed=1)
+    problem = problem_class(seed=1)
     design, _ = problem.random_design()
 
     # Test simulation outputs

From 4e4c9b086c177ac2e2a579a2814e42cb82ae4078 Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Tue, 2 Sep 2025 09:59:07 +0200
Subject: [PATCH 6/7] Add more prints to airfoil

---
 engibench/problems/airfoil/v0.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/engibench/problems/airfoil/v0.py b/engibench/problems/airfoil/v0.py
index c7ad3764..2f9d0cc3 100644
--- a/engibench/problems/airfoil/v0.py
+++ b/engibench/problems/airfoil/v0.py
@@ -624,13 +624,15 @@ def random_design(self, dataset_split: str = "train", design_key: str = "initial
     config = dataset["train"].select_columns(problem.conditions_keys)[idx]
 
     # Simulate the design
-    print(problem.simulate(design, config=config, mpicores=8))
+    print("Simulation results: ", problem.simulate(design, config=config, mpicores=8))
 
     # Cleanup the study directory; will delete the previous contents from simulate in this case
-    problem.reset(seed=0, cleanup=True)
+    problem.reset(seed=1, cleanup=True)
 
     # Get design and conditions from the dataset, render design
     opt_design, optisteps_history = problem.optimize(design, config=config, mpicores=8)
+    print("Optimized design: ", opt_design)
+    print("Optimization history: ", optisteps_history)
 
     # Render the final optimized design
     problem.render(opt_design, open_window=False, save=True)

From ce186a600114f3c52f0dbf4d59dfe86f16d53df9 Mon Sep 17 00:00:00 2001
From: Florian Felten <felten.florian@hotmail.fr>
Date: Tue, 2 Sep 2025 14:46:25 +0200
Subject: [PATCH 7/7] Update notebook

---
 tutorial.ipynb | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/tutorial.ipynb b/tutorial.ipynb
index 7c3dddee..46de1116 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -114,13 +114,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "44ed0ebe-b218-44ee-819b-7d211050b8c9",
    "metadata": {},
    "outputs": [],
    "source": [
-    "problem = Beams2D()\n",
-    "problem.reset(seed=9)"
+    "problem = Beams2D(seed=9)"
    ]
   },
   {
@@ -329,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "34b42db8",
    "metadata": {},
    "outputs": [
@@ -344,7 +343,10 @@
    "source": [
     "config = {\"volfrac\": 0.3, \"forcedist\": 0.5}\n",
     "violations = problem.check_constraints(my_design, config)\n",
-    "print(violations)"
+    "if violations:\n",
+    "    print(\"Violations found\", violations)\n",
+    "else:\n",
+    "    print(\"No violations found\")"
    ]
   },
   {
@@ -380,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "b7562ff7-1cb1-4538-b726-f2df8e18d980",
    "metadata": {},
    "outputs": [
@@ -396,6 +398,7 @@
     }
    ],
    "source": [
+    "problem.reset(seed=0)\n",
     "opt_design, opt_history = problem.optimize(my_design)\n",
     "opt_history[-1]"
    ]