physics-simulation

SimWorlds is a multi-agent framework that generates dynamic, editable 4D scenes from natural language, using Blender-specific procedural knowledge and a planner-coder-reviewer workflow, outperforming prior baselines.

This article presents a detailed benchmark comparing ECS and OOP architectures in JavaScript for a 2D physics simulation, testing memory locality and performance across multiple dimensions including broad-phase algorithms and sorting strategies, with results on an M4 Mac.

A comparison of four AI models (Fable 5, Opus 4.8, GLM 5.2, GPT 5.5) on generating HTML5 canvas physics demos shows Fable 5 outperforms others in quality but costs significantly more per test.

NVIDIA research presents Generative Pretrained Controllers (GPC), a method to pretrain motor control using discrete tokens and transformer-based next-token prediction, enabling fine-tuning for new tasks. Trained on 600+ hours of motion, GPC runs in real-time physics simulation for interactive control.

PhysiFormer uses coordinate-space diffusion to generate physically-plausible 3D object motions without explicit inductive biases, enabling efficient multi-object reasoning and generalization to complex materials and geometries.

Introduces Neural Particle Automata, a method for learning self-organizing particle dynamics using smooth particle hydrodynamics perception, enabling particles to have local perception vectors for an update rule, analogous to Neural Cellular Automata but on continuous particle positions.

This article introduces Environments AI, a tool that generates and runs code for physics simulations, enabling easier creation of simulation environments.

Z.ai releases GLM-5.2, an open-weights AI model with improved coding and agentic performance, demonstrated by beating Kimi K2.7 Code on a physics simulation benchmark across three tasks.

AdaVoMP uses a sparse adaptive voxel structure and transformer encoder-decoder to predict spatially-varying mechanical properties for 3D objects, enabling high-resolution deformable simulations with improved accuracy and efficiency.

A new arena lets LLMs control physics ragdolls in weapon duels where users define weapon damage zones, vote blind, and models battle for Elo. Free models like Llama 3.3 and GPT-OSS compete, with self-hostable infrastructure.

Proposes the first application of split conformal prediction to neural operator-based physics simulation, providing distribution-free prediction intervals with finite-sample coverage guarantees and adaptive-width intervals using MC Dropout uncertainty.

PhyGenHOI is a novel framework that generates physically accurate 4D human-object interactions by coupling motion diffusion models with material point method simulations using 3D Gaussian representations.

A blog post tutorial explaining how to implement Navier-Stokes fluid simulation in the Godot game engine, including code and mathematical explanations for learning purposes.

An experiment feeding GPT-4o, Claude 3.5 Sonnet, and other models the same double pendulum prompt reveals they pick opposite angle conventions, causing immediate visible mismatch in a shared renderer. The convention split, non-random across model families, suggests a bias in training data distribution for classical mechanics problems.

This paper presents an agentic framework that uses coding agents to generate physically plausible world simulations from natural language prompts, outperforming video-based models in physical accuracy and instruction fidelity.

RigidFormer is a new mesh-free, object-centric Transformer model that learns rigid dynamics from point clouds, outperforming mesh-based baselines in speed and scalability for multi-object contact dynamics.

Yann LeCun's team releases LeWorldModel, a tiny 15M-parameter physics model trained on a single GPU in hours that outperforms billion-dollar foundation models in planning speed and physical plausibility, challenging the dominant scaling paradigm.

PhysForge is a two-stage framework that generates interactive 3D assets with grounded physics and kinematic parameters, addressing the bottleneck of static geometry in virtual worlds.

Google DeepMind maintains MuJoCo, a high-performance open-source physics engine with C/Python APIs and Unity plugin for robotics and ML research.

The Well is a large-scale collection of 15TB of diverse physics simulation datasets across 16 domains, designed to benchmark machine learning surrogate models for spatiotemporal physical systems. It provides a unified PyTorch interface and example baselines to accelerate simulation-based workflows.