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  • 19 minutes ago
Researchers at MIT, the University of British Columbia and other institutions built WorldParticle, a deep-learning system that simulates physical materials ranging from sliding sand and sloshing fluids to folding proteins, MIT News reports. Rather than hand-coding a separate solver for each process, the system represents objects as tiny particles and predicts motion one step at a time, much like a chatbot predicts words. The model can adapt to materials and forces it never saw in training, and the team hopes it becomes a foundation model for simulation.

Disclosure: This video contains stock footage and content created or enhanced using AI-assisted tools.
Transcript
00:00What if one AI could simulate almost anything in the physical world?
00:03Right now, scientists need a different tool for each job.
00:07One simulator handles flowing water.
00:09Another models how a protein folds.
00:12Each is hand-coated around its own equations.
00:14Researchers at MIT and the University of British Columbia
00:18built a single system to replace that patchwork.
00:21It's called World Particle.
00:23It breaks every material into tiny particles,
00:26from a sand dune to a protein molecule.
00:28Then it predicts where each particle will move next, one step at a time.
00:33It works a lot like a chatbot predicting the next word.
00:36Because it learns the physics, it can handle materials it never saw before.
00:40Engineers could model oil sloshing against a valve,
00:43or sand shifting under a robot.
00:45The team hopes it becomes a GPT moment for simulation.
00:48Disclosure, this video contains stock footage
00:50and content created or enhanced using AI-assisted tools.

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