Physics-Informed Neural Networks (PINNs) - An Introduction - Ben Moseley | Jousef Murad
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- Опубликовано: 24 авг 2024
- 👉 PINNS in #MATLAB: • Physics-Informed Neura...
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Physics-informed neural networks (PINNs) offer a new and versatile approach for solving scientific problems by combining deep learning with known physical laws. Such networks are able to simulate physical systems, invert for their underlying parameters and even discover underlying physical laws themselves. In this introductory workshop and live coding session we will cover the basic definition of a PINN, their pros and cons compared to traditional scientific techniques and some of the state-of-the-art research in the field.
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![Physics Informed Neural Networks (PINNs) [Physics Informed Machine Learning]](http://i.ytimg.com/vi/-zrY7P2dVC4/mqdefault.jpg)
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🧠PINNS in MATLAB: ruclips.net/video/RTR_RklvAUQ/видео.html
+1 for Oxford PhD saying "timesing" instead of multiplying... respect! :D
Thanks for sharing this recording from the workshop. Thanks, Ben!
I love all of the questions!! 🤓 Ben is a great teacher!
at 14:30, it seems like external force will not operate on Unn. External force will be a constant term in the physics loss function.
But it is multiplying by U_NN term, so the loss can be derivate with respect to thega
Thank you for such an informative lecture on PINN.
Thanks for watching! :)
Very nice and clear presentation.
We are talking of relatively simple oscillator problem. How about if we have complex geometries for which FEM methods are most suited today? I have been reading of physics informed graph nets for the purpose of complex geomeries. Do you have any references for complex domains? Lets say i have a complex shaped mechanical component subjected to pressure fir which i normslly use FEM.?
Great video on this fascinating field. Thanks for sharing.
Sure :)
Nice lesson and clear presentation. Thank you!
A possibly useful method would be to have the neural network identify the invariants or a Lie group for a differential equation. Another approach, compute all scalar quantities and have neural network find the right combination of scalar quantities to find a Lagrangian for a physical system.
A great introduction and massive thanks for sharing the knowledge!
Fantastic introduction, much appreciated!
well done,the trend information is also very important,and it can be involved by a partial differential equation.i think maybe the parameters of the partial differential equation can also be the parameters of the neural network PINNS
Great work!
OMG, very cool video!!! The training performance is highly dependent on the "lambda" value, do you have ideas about how to define its value? Many thanks.
I wonder if this give better results with PDE for option pricing
Thanks for PINN , is code available ?
I think MIT developed something related to this, not sure whether it is opensource
nice tutorial. thank you.
could you please provide the example code of PINN?. Link in the comments not working.
Very nice lesson! I'm stuck on the Task 3 though, I can't get the network to converge for w0=80. Here's the code if anyone can spot what I'm missing here:
torch.manual_seed(123)
# define a neural network to train
pinn = FCN(1,1,32,3)
# define additional a,b learnable parameters in the ansatz
# TODO: write code here
a = torch.nn.Parameter(torch.zeros(1, requires_grad=True))
b = torch.nn.Parameter(torch.zeros(1, requires_grad=True))
# define boundary points, for the boundary loss
t_boundary = torch.tensor(0.).view(-1,1).requires_grad_(True)
# define training points over the entire domain, for the physics loss
t_physics = torch.linspace(0,1,60).view(-1,1).requires_grad_(True)
# train the PINN
d, w0 = 2, 80# note w0 is higher!
mu, k = 2*d, w0**2
t_test = torch.linspace(0,1,300).view(-1,1)
u_exact = exact_solution(d, w0, t_test)
# add a,b to the optimiser
# TODO: write code here
optimiser = torch.optim.Adam(list(pinn.parameters())+[a]+[b],lr=1e-3)
for i in range(15001):
optimiser.zero_grad()
# compute each term of the PINN loss function above
# using the following hyperparameters:
lambda1, lambda2 = 1e-1, 1e-4
# compute boundary loss
# TODO: write code here (change to ansatz formulation)
u = pinn(t_boundary)*torch.sin(a*t_boundary+b)
loss1 = (torch.squeeze(u) - 1)**2
dudt = torch.autograd.grad(u, t_boundary, torch.ones_like(u), create_graph=True)[0]
loss2 = (torch.squeeze(dudt) - 0)**2
# compute physics loss
# TODO: write code here (change to ansatz formulation)
u = pinn(t_physics)*torch.sin(a*t_physics+b)
dudt = torch.autograd.grad(u, t_physics, torch.ones_like(u), create_graph=True)[0]
d2udt2 = torch.autograd.grad(dudt, t_physics, torch.ones_like(dudt), create_graph=True)[0]
loss3 = torch.mean((d2udt2 + mu*dudt + k*u)**2)
# backpropagate joint loss, take optimiser step
# TODO: write code here
loss = loss1 + lambda1*loss2 + lambda2*loss3
loss.backward()
optimiser.step()
# plot the result as training progresses
if i % 5000 == 0:
#print(u.abs().mean().item(), dudt.abs().mean().item(), d2udt2.abs().mean().item())
u = (pinn(t_test)*torch.sin(a*t_test+b)).detach()
plt.figure(figsize=(6,2.5))
plt.scatter(t_physics.detach()[:,0],
torch.zeros_like(t_physics)[:,0], s=20, lw=0, color="tab:green", alpha=0.6)
plt.scatter(t_boundary.detach()[:,0],
torch.zeros_like(t_boundary)[:,0], s=20, lw=0, color="tab:red", alpha=0.6)
plt.plot(t_test[:,0], u_exact[:,0], label="Exact solution", color="tab:grey", alpha=0.6)
plt.plot(t_test[:,0], u[:,0], label="PINN solution", color="tab:green")
plt.title(f"Training step {i}")
plt.legend()
plt.show()
great work
similar question as some others. When we are solving even standard physics electrostatics, heat transfer etc, forget time domain, so only elliptic equations on complex CAD, I am wondering what applications can PINNs be used for. as opposed to using FEM. maybe shape optimization type problems? or inverse problems?
Where can we download the python script file
code link where can I get?
Hi Ben my Question is if I'm having an issue with audio and data strings bombardment maliciously engaging my synapse. Do you think fitting pinn's or over fitting pinn's to stabilise the nuclei would be the Answer. I've tried neural Clips and they come out/ tried Apache CNN and Hadoop to stabilise the nucleus. its been 4 years now and its very aggravating/infuriating and frustrating any help would be greatly appreciated
Im a beginner in PyTorch and OpenFOAM since the last few years, but today i learned that my "dream" is called "PINN" 🙂
Great 👍
Sure :)