Recent papers in Machine learning for computational science and engineering

1. LLM-driven design of physics-constrained constitutive models: Two agents are better than oneMachine learning for computational science and engineering · Thu,
2. Energy-based characterization of sparse sensor interactionsMachine learning for computational science and engineering · Wed,
3. Machine learning interatomic potential study of grain boundaries thermal conductance and tensile strength in hexagonal boron nitrideMachine learning for computational science and engineering · Thu,
4. Damage detection, localization, and sizing in composite structures using modal shape and multi-head convolutional neural networkMachine learning for computational science and engineering · Fri,
5. Decoding microstructure-property relationships in polycrystals: an explainable GNN surrogate for crystal plasticityMachine learning for computational science and engineering · Tue,
6. On-demand inverse design of metamaterials via denoising diffusion probabilistic modelsMachine learning for computational science and engineering · Wed,
7. Continual learning for autoregressive PDE surrogates under evolving physical regimesMachine learning for computational science and engineering · Sat,
8. Statistically grounded selection and validation of surrogate models for civil-engineering decision support: ANN, Kriging and SVM across three case studiesMachine learning for computational science and engineering · Wed,
9. Physics-informed hypernetworks for generalized obstacle-flow prediction with varying shapes and locationsMachine learning for computational science and engineering · Sat,
10. LLMs as designers of physics-constrained neural networks for constitutive modeling: a demonstration on hyperelastic solidsMachine learning for computational science and engineering · Mon,