10 papers
A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering
Krishna Kumar
Scientific machine learning (SciML) offers neural-network alternatives to numerical workflows in geotechnical engineering. This paper benchmarks multi-layer perceptrons (MLPs), phy…
Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators
Naveen Raj Manoharan, Hassan Iqbal, Krishna Kumar
Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizi…
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. Rathje
Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn…
Formal verification of tree-based machine learning models for lateral spreading
Krishna Kumar
Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased training data. Curre…
From images to properties: a NeRF-driven framework for granular material parameter inversion
Cheng-Hsi Hsiao, Krishna Kumar
We introduce a novel framework that integrates Neural Radiance Fields (NeRF) with Material Point Method (MPM) simulation to infer granular material properties from visual observati…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…