Github action: auto-update.

Author: github-actions[bot]

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -154,7 +154,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.2"
+      "version": "3.13.3"
     }
   },
   "nbformat": 4,

dev/_downloads/09a1631d14c6eb51e552aa53b9f6d3e8/checkpoint_FNO_darcy.ipynb

@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.2"
+      "version": "3.13.3"
     }
   },
   "nbformat": 4,

dev/_downloads/1015531de21015d6a3657fcb9124a684/plot_diffusion_advection_solver.zip

@@ -212,7 +212,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.2"
+      "version": "3.13.3"
     }
   },
   "nbformat": 4,

dev/_downloads/1bff93ede268f57f2123a573685b3499/plot_darcy_flow_spectrum.zip

@@ -0,0 +1,108 @@
+"""
+.. _burgers_2d_fd_vis :
+
+A Numerical Solver for Burgers' Equation in 2 Dimensions 
+========================================================
+An intro to our loss module's finite difference utility demonstrating
+its use to create a simple numerical solver for Burgers' equation in 2d.
+"""
+
+# %%
+# Import the library
+# ------------------
+# We first import our `neuralop` library and required dependencies.
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from neuralop.losses.finite_diff import central_diff_2d  
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# %%
+# Defining our problem
+# --------------------
+# We aim to solve the 2D viscous Burger's equations:
+#
+# u_t + u u_x + v u_y = nu (u_xx + u_yy)
+#
+# v_t + u v_x + v v_y = nu (v_xx + v_yy)
+
+## Simulation parameters
+Lx, Ly = 2.0, 2.0   # Domain lengths
+nx, ny = 64, 64   # Grid resolution
+T = 1    # Total simulation time
+dt = 0.001  # Time step
+nu = 0.04   # Viscosity
+
+## Create grid
+X = torch.linspace(0, Lx, nx, device=device).repeat(ny, 1).T 
+Y = torch.linspace(0, Ly, ny, device=device).repeat(nx, 1)  
+dx = Lx / (nx-1)
+dy = Ly / (ny-1)
+nt = int(T / dt)
+
+## Initial condition 
+u = -torch.sin(2 * np.pi * Y).to(device)
+v =  torch.cos(2 * np.pi * X).to(device)
+
+
+# %%
+# Simulate evolution using numerical solver
+# -----------------------------------------
+
+u_evolution = [u.clone()]
+v_evolution = [v.clone()]
+
+for _ in range(nt):
+    
+    # Compute first-order derivatives
+    u_x, u_y = central_diff_2d(u, [dx, dy])
+    v_x, v_y = central_diff_2d(v, [dx, dy])
+
+    # Compute second-order derivatives
+    u_xx, _ = central_diff_2d(u_x, [dx, dy])
+    _, u_yy = central_diff_2d(u_y, [dx, dy])
+    v_xx, _ = central_diff_2d(v_x, [dx, dy])
+    _, v_yy = central_diff_2d(v_y, [dx, dy])
+
+    # Evolve in time using Euler's method
+    u_next = u + dt * (-u * u_x - v * u_y + nu * (u_xx + u_yy))
+    v_next = v + dt * (-u * v_x - v * v_y + nu * (v_xx + v_yy))
+    
+    u, v = u_next.clone(), v_next.clone()
+    u_evolution.append(u.clone())
+    v_evolution.append(v.clone())
+
+u_evolution = torch.stack(u_evolution).cpu().numpy()
+v_evolution = torch.stack(v_evolution).cpu().numpy()
+
+# %%
+# Animating the solution
+# ----------------------
+
+num_frames = 100
+frame_indices = torch.linspace(0, len(u_evolution) - 1, num_frames, dtype=torch.int).cpu().numpy()
+u_frames = u_evolution[frame_indices]
+v_frames = v_evolution[frame_indices]
+
+fig, axs = plt.subplots(1, 2, figsize=(12, 6))
+cmap_u = axs[0].imshow(u_frames[0], extent=[0, Lx, 0, Ly], origin="lower", cmap="plasma")
+axs[0].set_title("Velocity u")
+plt.colorbar(cmap_u, ax=axs[0], shrink=0.75) 
+cmap_v = axs[1].imshow(v_frames[0], extent=[0, Lx, 0, Ly], origin="lower", cmap="plasma")
+axs[1].set_title("Velocity v")
+plt.colorbar(cmap_v, ax=axs[1], shrink=0.75) 
+
+def update(frame):
+    cmap_u.set_data(u_frames[frame])
+    cmap_v.set_data(v_frames[frame])
+    axs[0].set_title(f"Velocity u (Time: {frame_indices[frame] * dt:.3f})")
+    axs[1].set_title(f"Velocity v (Time: {frame_indices[frame] * dt:.3f})")
+    axs[0].set_xticks([])
+    axs[0].set_yticks([])
+    axs[1].set_xticks([])
+    axs[1].set_yticks([])
+    return cmap_u, cmap_v
+
+ani = animation.FuncAnimation(fig, update, frames=len(u_frames), interval=50, blit=False)

dev/_downloads/1c2e74cc4f68386faa92c3986ff5e279/plot_embeddings.ipynb

@@ -172,7 +172,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.2"
+      "version": "3.13.3"
     }
   },
   "nbformat": 4,