Physics Simulation is the computational modeling of physical systems and phenomena using mathematical representations of natural laws such as mechanics, thermodynamics, fluid dynamics, and electromagnetism. In AI research, physics simulation serves as a source of synthetic training data, a testbed for reinforcement learning agents, and a surrogate for expensive real-world experiments. Research directions include physics-informed neural networks, differentiable simulation, and learning-based surrogate models that approximate complex physical dynamics at reduced computational cost.

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When Video AI Gets Physics Wrong, the Consequences Are Real

Video generation models can look physically convincing while getting the physics completely wrong. PhyCo, new research from our Media Analytics department, introduces continuous, controllable physical properties to video AI, allowing practitioners to specify friction, bounce, and force.