12 stories tagged Physics
Researchers introduced EvoPINN, an AI system that uses evolutionary algorithms and large language models to automate the design of physics-informed neural networks (PINNs), eliminating manual tuning and enabling faster, more accessible solutions to complex physics problems.
Researchers introduced StatMechBench-v0, a new benchmark to test if LLM-based AI agents can transform complex physics problems into simpler, known models. The study evaluates a propose-verify-revise agent across multiple LLMs on six Ising-type problems.
CERN's Genesis Mission aims to develop and deploy self-improving AI models that can autonomously analyze particle physics data, potentially accelerating breakthroughs in high-energy physics and beyond.
Researchers developed PhyDrawGen, an AI system that generates physics diagrams from text while respecting physical laws. Unlike previous tools, it doesn't make common mistakes about forces or geometry. This could help students and teachers create precise diagrams for learning and teaching physics.
Scientists found that AI models often predict realistic-looking but physically impossible outcomes. They propose a new approach that focuses on understanding real-world physics to make AI more reliable.
Physicists have developed hybrid particles that combine light and matter, potentially replacing electrons in AI chips. This breakthrough could make AI systems faster and more energy-efficient. The discovery was published in a recent study and has sparked excitement in the tech community.
Researchers created a new test to see if AI can solve physics-based puzzles like humans. The test uses the classic game The Incredible Machine 2 to evaluate AI's logical reasoning skills.
Researchers developed a structured AI system called SCALAR that improves theoretical physics problem-solving. It uses a feedback loop where an AI proposes solutions, another critiques them, and a third evaluates the process. This could make advanced research more efficient and accessible.
Researchers propose a new way for AI to understand the world by combining physics with predictive models. This could make AI systems like robots and self-driving cars smarter and more adaptable.
Researchers introduce FormalScience, an AI pipeline that helps domain experts formalize scientific reasoning into verifiable code. This addresses a key challenge in applying LLMs to scientific domains with complex notations.
Researchers have developed a method to explore high-dimensional PDE solution spaces using latent foundation models. This approach promises to revolutionize the study of complex physical phenomena by enabling automated, large-scale exploration.
Researchers developed a model to infer analytical solutions from visualizations of physical fields. This breakthrough could revolutionize AI-assisted scientific discovery.