AIBullisharXiv – CS AI · Jun 237/10
🧠Researchers introduce GyroSwin, a neural surrogate model that simulates 5D gyrokinetic plasma turbulence with 1000x computational efficiency while capturing nonlinear physics effects. This breakthrough combines hierarchical Vision Transformers with cross-attention mechanisms to predict turbulent heat transport more accurately than traditional reduced-order models, advancing nuclear fusion energy research.
AIBullisharXiv – CS AI · Apr 157/10
🧠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
🧠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.
AINeutralarXiv – CS AI · Jun 256/10
🧠Researchers present a novel gradient-based inverse lithography technology (ILT) for extreme ultraviolet (EUV) masks that uses physics-informed neural operators and automatic differentiation to optimize mask absorber permittivity. The method combines a differentiable waveguide approach with waveguide neural operators (WGNO) to recover mask structures achieving desired field patterns on wafers, demonstrated on realistic 2D and 3D absorbers at 11.2 nm wavelengths.
AINeutralOpenAI News · Jun 115/10
🧠Astrophysicist Chi-kwan Chan leverages OpenAI's Codex to accelerate black hole simulations, enabling researchers to efficiently model extreme gravitational phenomena and validate Einstein's general relativity predictions. This application demonstrates how AI-assisted coding tools enhance scientific computing workflows in fundamental physics research.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers introduce OnlyDense, a machine learning framework that reduces computational costs for Lagrangian particle simulation methods like SPH and MPM by representing massive particle systems as functions in Hilbert space rather than discrete particle sets. The method achieves 0.99+ R² accuracy using just 32 basis functions on million-particle simulations, combining classical reduced-order modeling with deep learning.
AIBullisharXiv – CS AI · May 126/10
🧠Researchers introduce SuperMeshNet, a semi-supervised neural network framework that dramatically reduces the amount of expensive high-resolution training data needed for mesh-based simulations. By combining small paired datasets with abundant unpaired data through complementary learning, the system achieves superior accuracy while requiring 90% less supervised training data than fully supervised approaches.
AIBullisharXiv – CS AI · Mar 45/102
🧠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
🧠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
🧠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
🧠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.
