701046661f
* Solvers for multiple models - Implementing the possibility to add multiple models for solvers (e.g. GAN) - Implementing GAROM solver, see https://arxiv.org/abs/2305.15881 - Implementing tests for GAROM solver (cpu only) - Fixing docs PINNs - Creating a solver directory, for consistency in the package --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-040.eduroam.sissa.it>
215 lines
7.6 KiB
Python
215 lines
7.6 KiB
Python
import torch
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import pytest
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from pina.problem import SpatialProblem
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from pina.operators import nabla
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from pina.geometry import CartesianDomain
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from pina import Condition, LabelTensor, PINN
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from pina.trainer import Trainer
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from pina.model import FeedForward
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from pina.equation.equation import Equation
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from pina.equation.equation_factory import FixedValue
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from pina.plotter import Plotter
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from pina.loss import LpLoss
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def laplace_equation(input_, output_):
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force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
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torch.sin(input_.extract(['y'])*torch.pi))
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nabla_u = nabla(output_.extract(['u']), input_)
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return nabla_u - force_term
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my_laplace = Equation(laplace_equation)
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in_ = LabelTensor(torch.tensor([[0., 1.]], requires_grad=True), ['x', 'y'])
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out_ = LabelTensor(torch.tensor([[0.]], requires_grad=True), ['u'])
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class Poisson(SpatialProblem):
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output_variables = ['u']
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spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
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conditions = {
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'gamma1': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 1}),
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equation=FixedValue(0.0)),
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'gamma2': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': 0}),
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equation=FixedValue(0.0)),
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'gamma3': Condition(
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location=CartesianDomain({'x': 1, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'gamma4': Condition(
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location=CartesianDomain({'x': 0, 'y': [0, 1]}),
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equation=FixedValue(0.0)),
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'D': Condition(
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location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}),
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equation=my_laplace),
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'data': Condition(
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input_points=in_,
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output_points=out_)
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}
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def poisson_sol(self, pts):
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return -(
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torch.sin(pts.extract(['x'])*torch.pi) *
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torch.sin(pts.extract(['y'])*torch.pi)
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)/(2*torch.pi**2)
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truth_solution = poisson_sol
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class myFeature(torch.nn.Module):
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"""
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Feature: sin(x)
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"""
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def __init__(self):
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super(myFeature, self).__init__()
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def forward(self, x):
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t = (torch.sin(x.extract(['x'])*torch.pi) *
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torch.sin(x.extract(['y'])*torch.pi))
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return LabelTensor(t, ['sin(x)sin(y)'])
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# make the problem
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poisson_problem = Poisson()
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model = FeedForward(len(poisson_problem.input_variables),len(poisson_problem.output_variables))
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model_extra_feats = FeedForward(len(poisson_problem.input_variables)+1,len(poisson_problem.output_variables))
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extra_feats = [myFeature()]
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def test_constructor():
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PINN(problem = poisson_problem, model=model, extra_features=None)
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def test_constructor_extra_feats():
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model_extra_feats = FeedForward(len(poisson_problem.input_variables)+1,len(poisson_problem.output_variables))
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PINN(problem = poisson_problem, model=model_extra_feats, extra_features=extra_feats)
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def test_train_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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poisson_problem.discretise_domain(n, 'grid', locations=['D'])
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pinn = PINN(problem = poisson_problem, model=model, extra_features=None, loss=LpLoss())
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trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
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trainer.train()
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def test_train_extra_feats_cpu():
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poisson_problem = Poisson()
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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poisson_problem.discretise_domain(n, 'grid', locations=boundaries)
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poisson_problem.discretise_domain(n, 'grid', locations=['D'])
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pinn = PINN(problem = poisson_problem, model=model_extra_feats, extra_features=extra_feats)
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trainer = Trainer(solver=pinn, kwargs={'max_epochs' : 5, 'accelerator':'cpu'})
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trainer.train()
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"""
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def test_train_2():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(problem, model)
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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def test_train_extra_feats():
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pinn = PINN(problem, model_extra_feat, [myFeature()])
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(5)
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def test_train_2_extra_feats():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(problem, model_extra_feat, [myFeature()])
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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def test_train_with_optimizer_kwargs():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3})
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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def test_train_with_lr_scheduler():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(
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problem,
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model,
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lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
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lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
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)
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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# def test_train_batch():
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# pinn = PINN(problem, model, batch_size=6)
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(5)
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# def test_train_batch_2():
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 10
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# expected_keys = [[], list(range(0, 50, 3))]
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# param = [0, 3]
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# for i, truth_key in zip(param, expected_keys):
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# pinn = PINN(problem, model, batch_size=6)
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(50, save_loss=i)
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# assert list(pinn.history_loss.keys()) == truth_key
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if torch.cuda.is_available():
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# def test_gpu_train():
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# pinn = PINN(problem, model, batch_size=20, device='cuda')
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# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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# n = 100
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# pinn.discretise_domain(n, 'grid', locations=boundaries)
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# pinn.discretise_domain(n, 'grid', locations=['D'])
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# pinn.train(5)
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def test_gpu_train_nobatch():
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pinn = PINN(problem, model, batch_size=None, device='cuda')
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 100
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pinn.discretise_domain(n, 'grid', locations=boundaries)
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pinn.discretise_domain(n, 'grid', locations=['D'])
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pinn.train(5)
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""" |