physics-simulation

GLM-5.3 outperforms Space Bunny Alpha in a 5-scene physics test conducted by AI/ML API, highlighting strengths and weaknesses in AI physics simulation.

PolyBridgeBench is a new executable benchmark for evaluating multimodal LLMs on physics-grounded bridge design tasks, revealing gaps between deterministic validity and dynamic success in structure synthesis and repair.

The article focuses on relativistic raytracing, a technique that combines raytracing with relativistic physics to improve simulation accuracy in computer graphics and scientific visualization.

REVERSAL-BENCH introduces a benchmark for measuring reversibility in reset-free reinforcement learning, revealing a 'reversibility cliff' where agents fail in irrecoverable states. The benchmark includes a continuous reversibility parameter and a reset oracle to test state recoverability across multiple environments.

DeformSmith is a framework for generating interactive, physically credible deformable assets for robot manipulation from text or images, using physics-guided hierarchical generation to improve quality and plausibility.

The paper introduces causal writability in video models, showing that correct physical motion remains available but becomes uneditable after a critical depth, which impacts how training corrects errors.

This paper benchmarks various optimizers for solving inverse problems with differentiable physics simulators, comparing their performance across different physics domains and problem types to provide insights for optimizer selection and design.

SNAP3D introduces a physics-guided framework to improve part-aware 3D generation from a single image, ensuring stable and physically valid assemblies through simulation feedback and resolving issues like inter-part penetration.

TRACE is a graph neural network simulator for granular dynamics that uses contact-edge memory to improve accuracy and efficiency, reducing long-rollout errors by 31–62% and achieving speedups over traditional solvers.

Puffin-World is a unified multimodal framework that integrates physical understanding, spatial simulation, and 3D world generation using an Omni-Camera representation, scaled with the Puffin-16M dataset.

Ako AI is an AI-powered online simulation tool that generates interactive physics lesson plans, allowing teachers to create draggable experiments for concepts like convex lens imaging.

Newton's Orchard is an open-source browser-based simulation playground for exploring space and gravity, built as an educational tool with features like deterministic time scrubbing, editable bodies, and missions.

Meta Reality Labs Research has open-sourced Project SuperDex, a contact-first physics engine and platform for simulating dexterous manipulation with advanced contact simulation and VR-based data generation.

This paper introduces ShuttleArena, a physics-based self-play environment for badminton where agents learn interpretable tactical policies using PPO, showing competitive improvement in shot selection and recovery.

Anima Anandkumar announces the launch of startup Accelerated Understanding, which is training large-scale AI models to simulate and understand physics in 4D, aiming to accelerate invention and discovery by pushing context lengths to trillions.

A developer introduces FieldGeo, a set of AI models that encode geometry, physics, and design for manufacturing into a latent space, aiming to enhance generative design with better spatial understanding beyond traditional AI CAD approaches.

An early-stage project is building an open, browser-based arena for AI agents to compete in real-time physical reasoning tasks, demonstrated by an AI agent achieving 100% task completion and high spatial accuracy in a block-stacking simulation.

The article highlights LLMs' lack of innate physical world understanding and introduces a Fourier Neural Operator-based framework that accelerates quantum dynamics prediction by 10^7 times, enabling efficient inverse design of quantum control protocols with improved success rates.

HI-MGN introduces a hierarchical multiscale graph neural network to improve long-range communication in mesh-based physics simulations, enhancing accuracy while reducing training time and memory usage compared to existing methods.

Eigendrum is a web tool that allows users to draw shapes and hear them as real drums by solving eigenvalue problems for drumhead vibrations using finite element methods.