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…
Experiment-informed finite-strain inverse design of spinodal metamaterials
Prakash Thakolkaran, Michael A. Espinal, Somayajulu Dhulipala +2
Spinodal metamaterials, with architectures inspired by natural phase-separation processes, have presented a significant alternative to periodic and symmetric morphologies when desi…
Seismological Understanding of Accelerogram Amplitude Scaling for Engineers with Implications to Seismic Risk Analysis
Somayajulu L. N. Dhulipala
Due to the paucity of strong recorded accelerograms, earthquake engineering analysis relies on accelerogram amplitude scaling for structural damage/collapse assessment and target s…
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che +4
Estimating the probability of failure for complex real-world systems using high-fidelity computational models is often prohibitively expensive, especially when the probability is s…
Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel
Somayajulu L. N. Dhulipala, Promit Chakroborty, Michael D. Shields +3
The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel proposed to be used for multiple modern nuclear technologies. Therefore, characterizing its safety…
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in pr…
Bayesian Inference with Latent Hamiltonian Neural Networks
Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
When sampling for Bayesian inference, one popular approach is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS) which automatically decides the end…
Efficient Interdependent Systems Recovery Modeling with DeepONets
Somayajulu L. N. Dhulipala, Ryan C. Hruska
Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the r…
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
Somayajulu L. N. Dhulipala, Michael D. Shields, Promit Chakroborty +7
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However,…
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation
S. L. N. Dhulipala, M. D. Shields, B. W. Spencer +4
While multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by a…