#computational-physics News & Analysis

6 articles tagged with #computational-physics. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

6 articles

AIBullisharXiv – CS AI · 3d ago7/10

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Towards grounded autonomous research: an end-to-end LLM mini research loop on published computational physics

Researchers demonstrate an autonomous LLM agent capable of executing a complete research loop—reading, reproducing, critiquing, and extending computational physics papers. Testing across 111 papers reveals the agent identifies substantive flaws in 42% of cases, with 97.7% of issues requiring actual computation to detect, and produces a publishable peer-review comment on a Nature Communications paper without human direction.

AIBullisharXiv – CS AI · Mar 46/104

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Large Electron Model: A Universal Ground State Predictor

Researchers introduce Large Electron Model, a neural network that uses Fermi Sets architecture to predict ground state wavefunctions of interacting electrons across different Hamiltonian parameters. The model demonstrates accurate predictions for up to 50 particles and generalizes across unseen coupling strengths, potentially advancing material discovery beyond density functional theory limitations.

AIBullisharXiv – CS AI · Mar 45/102

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Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results

Researchers have developed Domain-aware Fourier Features (DaFFs) to enhance Physics-Informed Neural Networks (PINNs), achieving orders-of-magnitude lower errors and faster convergence. The approach incorporates domain-specific characteristics like geometry and boundary conditions while eliminating the need for explicit boundary condition loss terms, making PINNs more accurate, efficient, and interpretable.

AINeutralIEEE Spectrum – AI · Feb 236/108

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AI’s Math Tricks Don’t Work for Scientific Computing

AI engineer Laslo Hunhold has developed 'takums,' a new number format specifically designed for scientific computing that maintains dynamic range when using fewer bits. Unlike AI-optimized formats that work well for machine learning but fail in scientific applications, takums address the unique computational needs of physics, biology, and engineering simulations.

AINeutralOpenAI News · Mar 44/102

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Extending single-minus amplitudes to gravitons

A new research preprint demonstrates the extension of single-minus amplitudes to gravitons, with AI assistance from GPT-5.2 Pro used to derive and verify nonzero graviton tree amplitudes in quantum gravity calculations.

AINeutralarXiv – CS AI · Mar 34/105

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Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction

Researchers have developed Fisale, a new AI framework for modeling complex fluid-solid interactions using neural networks inspired by classical Arbitrary Lagrangian-Eulerian methods. The system addresses limitations in existing deep learning approaches by enabling two-way interactions between fluids and solids with unified geometry-aware embeddings.