From ac6b73ef9c83eb4c48b4a20fbe757976d703694b Mon Sep 17 00:00:00 2001
From: Mark Saroufim <marksaroufim@gmail.com>
Date: Wed, 25 Jun 2025 13:19:49 -0700
Subject: [PATCH] Revert "Add Differentiable Physics: Mass-Spring System
 example (#1359)"

This reverts commit 7c35995e4ca3fc2d1ad0c8e3497f6d1d0736762a.
---
 differentiable_physics/mass_spring.py      | 154 ---------------------
 differentiable_physics/mass_spring_viz.png | Bin 17726 -> 0 bytes
 differentiable_physics/readme.md           |  68 ---------
 differentiable_physics/requirements.txt    |   3 -
 run_python_examples.sh                     |   6 -
 5 files changed, 231 deletions(-)
 delete mode 100644 differentiable_physics/mass_spring.py
 delete mode 100644 differentiable_physics/mass_spring_viz.png
 delete mode 100644 differentiable_physics/readme.md
 delete mode 100644 differentiable_physics/requirements.txt

diff --git a/differentiable_physics/mass_spring.py b/differentiable_physics/mass_spring.py
deleted file mode 100644
index e7d83d487f..0000000000
--- a/differentiable_physics/mass_spring.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import argparse
-import matplotlib.pyplot as plt
-import os
-
-
-class MassSpringSystem(nn.Module):
-    def __init__(self, num_particles, springs, mass=1.0, dt=0.01, gravity=9.81, device="cpu"):
-        super().__init__()
-        self.device = device
-        self.mass = mass
-        self.springs = springs
-        self.dt = dt
-        self.gravity = gravity
-
-        # Particle 0 is fixed at the origin
-        self.initial_position_0 = torch.tensor([0.0, 0.0], device=device)
-
-        # Remaining particles are trainable
-        self.initial_positions_rest = nn.Parameter(torch.randn(num_particles - 1, 2, device=device))
-
-        # Velocities
-        self.velocities = torch.zeros(num_particles, 2, device=device)
-
-    def forward(self, steps):
-        positions = torch.cat([self.initial_position_0.unsqueeze(0), self.initial_positions_rest], dim=0)
-        velocities = self.velocities
-
-        for _ in range(steps):
-            forces = torch.zeros_like(positions)
-
-            # Compute spring forces
-            for (i, j, rest_length, stiffness) in self.springs:
-                xi, xj = positions[i], positions[j]
-                dir_vec = xj - xi
-                dist = dir_vec.norm()
-                force = stiffness * (dist - rest_length) * dir_vec / (dist + 1e-6)
-                forces[i] += force
-                forces[j] -= force
-
-            # Apply gravity
-            forces[:, 1] -= self.gravity * self.mass
-
-            # Semi-implicit Euler integration
-            acceleration = forces / self.mass
-            velocities = velocities + acceleration * self.dt
-            positions = positions + velocities * self.dt
-
-            # Fix particle 0 at origin
-            positions[0] = self.initial_position_0
-            velocities[0] = torch.tensor([0.0, 0.0], device=positions.device)
-
-        return positions
-
-
-def visualize_positions(initial, final, target, save_path="mass_spring_viz.png"):
-    plt.figure(figsize=(6, 4))
-    plt.scatter(initial[:, 0], initial[:, 1], c='blue', label='Initial', marker='x')
-    plt.scatter(final[:, 0], final[:, 1], c='green', label='Final', marker='o')
-    plt.scatter(target[:, 0], target[:, 1], c='red', label='Target', marker='*')
-    plt.title("Mass-Spring System Positions")
-    plt.xlabel("X")
-    plt.ylabel("Y")
-    plt.legend()
-    plt.grid(True)
-    plt.tight_layout()
-    plt.savefig(save_path)
-    print(f"Saved visualization to {os.path.abspath(save_path)}")
-    plt.close()
-
-
-def train(args):
-    #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    device = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else torch.device("cpu")
-    print(f"Using device: {device}")
-    system = MassSpringSystem(
-        num_particles=args.num_particles,
-        springs=[(0, 1, 1.0, args.stiffness)],
-        mass=args.mass,
-        dt=args.dt,
-        gravity=args.gravity,
-        device=device,
-    )
-
-    optimizer = optim.Adam(system.parameters(), lr=args.lr)
-    target_positions = torch.tensor(
-        [[0.0, 0.0], [1.0, 0.0]], device=device
-    )
-
-    for epoch in range(args.epochs):
-        optimizer.zero_grad()
-        final_positions = system(args.steps)
-        loss = (final_positions - target_positions).pow(2).mean()
-        loss.backward()
-        optimizer.step()
-
-        if (epoch + 1) % args.log_interval == 0:
-            print(f"Epoch {epoch+1}/{args.epochs}, Loss: {loss.item():.6f}")
-
-    # Visualization
-    initial_positions = torch.cat([system.initial_position_0.unsqueeze(0), system.initial_positions_rest.detach()], dim=0).cpu().numpy()
-    visualize_positions(initial_positions, final_positions.detach().cpu().numpy(), target_positions.cpu().numpy())
-
-    print("\nTraining completed.")
-    print(f"Final positions:\n{final_positions.detach().cpu().numpy()}")
-    print(f"Target positions:\n{target_positions.cpu().numpy()}")
-
-
-def evaluate(args):
-    #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    device = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else torch.device("cpu")
-    print(f"Using device: {device}")
-    system = MassSpringSystem(
-        num_particles=args.num_particles,
-        springs=[(0, 1, 1.0, args.stiffness)],
-        mass=args.mass,
-        dt=args.dt,
-        gravity=args.gravity,
-        device=device,
-    )
-
-    with torch.no_grad():
-        final_positions = system(args.steps)
-        print(f"Final positions after {args.steps} steps:\n{final_positions.cpu().numpy()}")
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Differentiable Physics: Mass-Spring System")
-    parser.add_argument("--epochs", type=int, default=1000, help="Number of training epochs")
-    parser.add_argument("--steps", type=int, default=50, help="Number of simulation steps per forward pass")
-    parser.add_argument("--lr", type=float, default=0.01, help="Learning rate")
-    parser.add_argument("--dt", type=float, default=0.01, help="Time step for integration")
-    parser.add_argument("--mass", type=float, default=1.0, help="Mass of each particle")
-    parser.add_argument("--stiffness", type=float, default=10.0, help="Spring stiffness constant")
-    parser.add_argument("--num_particles", type=int, default=2, help="Number of particles in the system")
-    parser.add_argument("--mode", choices=["train", "eval"], default="train", help="Mode: train or eval")
-    parser.add_argument("--log_interval", type=int, default=100, help="Print loss every n epochs")
-    parser.add_argument("--gravity", type=float, default=9.81, help="Gravity strength")
-    return parser.parse_args()
-
-
-def main():
-    args = parse_args()
-
-    if args.mode == "train":
-        train(args)
-    elif args.mode == "eval":
-        evaluate(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/differentiable_physics/mass_spring_viz.png b/differentiable_physics/mass_spring_viz.png
deleted file mode 100644
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diff --git a/differentiable_physics/readme.md b/differentiable_physics/readme.md
deleted file mode 100644
index 452eb5d374..0000000000
--- a/differentiable_physics/readme.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# Differentiable Physics: Mass-Spring System
-
-This example demonstrates a simple differentiable **mass-spring system** using PyTorch.
-
-A set of particles is connected via springs and evolves over time under the influence of:
-- **Spring forces** (via Hooke’s Law)
-- **Gravity** (acting in the negative Y-direction)
-
-The system is fully differentiable, enabling **gradient-based optimization** of the **initial positions** of the particles so that their **final positions** match a desired **target configuration**.
-
-This idea is inspired by differentiable simulation frameworks such as those presented in recent research (see reference below).
-
----
-
-##  Files
-
-- `mass_spring.py` — Implements the simulation, training loop, and evaluation logic.
-- `README.md` — Description, instructions, and visualization output.
-- `mass_spring_viz.png` — Output visualization of the final vs target configuration.
-
----
-
-##  Key Concepts
-
-| Term              | Description                                                                 |
-|-------------------|-----------------------------------------------------------------------------|
-| Initial Position  | Learnable 2D coordinates (x, y) of each particle before simulation begins.  |
-| Target Position   | Desired final 2D position after simulation. Used to compute loss.           |
-| Gravity           | Constant force `[0, -9.8]` pulling particles downward in Y direction.       |
-| Spring Forces     | Modeled using Hooke’s Law. Particles connected by springs exert forces.     |
-| Dimensionality    | All particle positions and forces are 2D vectors.                           |
-
----
-
-## Requirements
-
-- Python 3.8+
-- PyTorch ≥ 2.0
-
-Install requirements (if needed):
-
-pip install -r requirements.txt
-
-
-## Usage
-
-First, ensure PyTorch is installed.
-
-#### Train the system
-
-
-python mass_spring.py --mode train
-
-
-![Mass-Spring System Visualization](mass_spring_viz.png)
-
-*Mass-Spring System Visualization comparing final vs target positions.*
-
-
-
-## References
-
-[1] Sanchez-Gonzalez, A. et al. (2020).  
-Learning to Simulate Complex Physics with Graph Networks.  
-arXiv preprint arXiv:2002.09405.  
-Available: https://arxiv.org/abs/2002.09405
-
-
diff --git a/differentiable_physics/requirements.txt b/differentiable_physics/requirements.txt
deleted file mode 100644
index f88df426f8..0000000000
--- a/differentiable_physics/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-torch>=2.6
-matplotlib
-
diff --git a/run_python_examples.sh b/run_python_examples.sh
index a52c026464..c9c90f9552 100755
--- a/run_python_examples.sh
+++ b/run_python_examples.sh
@@ -170,11 +170,6 @@ function gat() {
   uv run main.py --epochs 1 --dry-run || error "graph attention network failed"
 }
 
-function differentiable_physics() {
-  uv run mass_spring.py --mode train --epochs 5 --steps 3 || error "differentiable_physics example failed"
-}
-
-
 eval "base_$(declare -f stop)"
 
 function stop() {
@@ -228,7 +223,6 @@ function run_all() {
   run fx
   run gcn
   run gat
-  run differentiable_physics
 }
 
 # by default, run all examples