9 citations · 35 across the 28 of their papers we have counts for
6 papers · 1 filter
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…
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…
Investigating the effect of CPT in lateral spreading prediction using Explainable AI
Cheng-Hsi Hsiao, Ellen Rathje, Krishna Kumar
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an AI model. We em…
Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
Brent Sordo, Ellen Rathje, Krishna Kumar
This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key inputs to th…