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Add physical context to examples, and use backend-agnostic functions #2066

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Add physical context to examples, and use backend-agnostic functions #2066

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1508cad
Refractor diffusion_1d example with physical context and descriptive …
vishnu-2206 Mar 19, 2026
64a176f
chore: use dde.backend for source term and revert docstring changes
vishnu-2206 Mar 19, 2026
f1fbe3a
fix: unify 1D diffusion suite with universal dde.backend and physics …
vishnu-2206 Mar 19, 2026
f355cb5
fix: refactor 1D diffusion example to be backend-agnostic using dde.b…
vishnu-2206 Mar 19, 2026
f0d08ae
fix: update exactBC.py with backend-agnostic transform and approved s…
vishnu-2206 Mar 19, 2026
5321b8f
add 1D heat equation example with adaptive resampling
vishnu-2206 Mar 21, 2026
b2f54be
Remove redundant backend labels and cleanup comments
CarrotBear-00 Apr 3, 2026
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13 changes: 8 additions & 5 deletions examples/pinn_forward/diffusion_1d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,27 +18,30 @@ def pde(x, y):
# Backend jax
# dy_t, _ = dde.grad.jacobian(y, x, j=1)
# dy_xx, _ = dde.grad.hessian(y, x, j=0)
# Cross-backend source term
f = dde.backend.exp(-x[:, 1:]) * (
dde.backend.sin(np.pi * x[:, 0:1]) - np.pi**2 * dde.backend.sin(np.pi * x[:, 0:1])
)
# Backend tensorflow.compat.v1 or tensorflow
return (
dy_t
- dy_xx
+ tf.exp(-x[:, 1:])
* (tf.sin(np.pi * x[:, 0:1]) - np.pi ** 2 * tf.sin(np.pi * x[:, 0:1]))
)
+ f
)
# Backend pytorch
# return (
# dy_t
# - dy_xx
# + torch.exp(-x[:, 1:])
# * (torch.sin(np.pi * x[:, 0:1]) - np.pi ** 2 * torch.sin(np.pi * x[:, 0:1]))
# )
# )
# Backend jax
# return (
# dy_t
# - dy_xx
# + jnp.exp(-x[:, 1:])
# * (jnp.sin(np.pi * x[..., 0:1]) - np.pi ** 2 * jnp.sin(np.pi * x[..., 0:1]))
# )
# )
# Backend paddle
# return (
# dy_t
Expand Down