Papers

• An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks (2026)
  Joseph Webb et al.
  5.01
• A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction (2026)
  Xin-Yang Liu et al.
  4.39
• Grounded autonomous research: a fault-tolerant LLM pipeline from corpus to manuscript in frontier computational physics (2026)
  Haonan Huang
  4.39
• Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials (2026)
  Gil Harari et al.
  3.51
• Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems (2026)
  Xiao Zhu et al.
  2.00
• Latent PDE mapping for efficient physics-informed learning across geometries with limited data (2026)
  Ingvild Askim Adde et al.
  2.00