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```python
def train(loader, model, optimizer, total_epochs):
model.train()
global bce_loss_lst, iou_loss_lst, ssim_loss_lst, adapt_weights
step = 0
for current_epoch in range(tota…
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Stiernström, Vidar and Almquist, Martin and Mattsson, Ken Roger, Boundary-Optimized Summation-by-Parts Operators for Finite Difference Approximations of Second Derivatives with Variable Coefficients.…
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Here is the code
"""
@time p = petlion(
NMC;
aging =:SEI,
N_p = 10, # discretizations in the cathode
N_s = 10, # discretizations in the separator
N_n = 10, # discretization…
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It would be quite incredible if one could make the finite difference coefficients handle vector-valued functions/operators well. For example, the Jacobian.
One difficulty will be that the number o…
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[#2792 (comment)](https://github.com/NanoComp/meep/pull/2792#issuecomment-2171942477) identified a bug in the adjoint gradients in 2D for the $\mathcal{P}$ polarization (electric field in the plane). …
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Based on discussions with @mezzarobba and @fredrik-johansson:
- [ ] A module for generic Ore polynomials: `gr_ore_poly`
- [ ] differential operators in `d/dz` and in `z*d/dz`, with `gr_poly`s as…
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The pricing policy has parameters $\theta$s, and our goal is to optimized the simulation in order to produce max profits.
To do so, we need to calculate gradient of objective function(profit) w.r…
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The coefficient furthest to the right is not being replaced when using the right sided finite difference term 'Function'.dxr with symbolic coefficients. Hence the presence of the w symbol causing the …
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Compact finite difference schemes are families of high-order implicit operators for derivatives on structured rectilinear or curvilinear grids, most commonly used for hyperbolic PDEs for their favorab…
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This is a simplified version of Homer's adjoint variable method. Homer's current version has several great features, but the core functionality still seems slightly buggy. Specifically, the adjoint gr…