42 citations · 88 across the 15 of their papers we have counts for
15 papers
Compositional Generation for Long-Horizon Coupled PDEs
Somayajulu L. N. Dhulipala, Deep Ray, Nicholas Forman
Simulating coupled PDE systems is computationally intensive, and prior efforts have largely focused on training surrogates on the joint (coupled) data, which requires a large amoun…
Curvature-Guided Mechanics and Design of Spinodal and Shell-Based Architected Materials
Somayajulu Dhulipala, Carlos M. Portela
Additively manufactured (AM) architected materials have enabled unprecedented control over mechanical properties of engineered materials. While lattice architectures have played a…
MOOSE ProbML: Parallelized Probabilistic Machine Learning and Uncertainty Quantification for Computational Energy Applications
Somayajulu L. N. Dhulipala, Peter German, Yifeng Che +5
This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphy…
Quantifying Model Uncertainty of Neural Network-based Turbulence Closures
Cody Grogan, Som Dutta, Mauricio Tano +2
With increasing computational demand, Neural-Network (NN) based models are being developed as pre-trained surrogates for different thermohydraulics phenomena. An area where this ap…
Covariance-free Bi-fidelity Control Variates Importance Sampling for Rare Event Reliability Analysis
Promit Chakroborty, Somayajulu L. N. Dhulipala, Michael D. Shields
Multifidelity modeling has been steadily gaining attention as a tool to address the problem of exorbitant model evaluation costs that makes the estimation of failure probabilities…
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks
Denny Thaler, Somayajulu L. N. Dhulipala, Franz Bamer +2
We present a new Subset Simulation approach using Hamiltonian neural network-based Monte Carlo sampling for reliability analysis. The proposed strategy combines the superior sampli…