Articles with public access mandates - Nishant PandaLearn more
Not available anywhere: 4
A hybridized discontinuous Galerkin method for the nonlinear Korteweg–de Vries equation
A Samii, N Panda, C Michoski, C Dawson
Journal of Scientific Computing 68, 191-212, 2016
Mandates: US National Science Foundation
A probabilistic clustering approach for identifying primary subnetworks of discrete fracture networks with quantified uncertainty
D Osthus, JD Hyman, S Karra, N Panda, G Srinivasan
SIAM/ASA Journal on Uncertainty Quantification 8 (2), 573-600, 2020
Mandates: US Department of Energy
A Stochastic Inverse Problem for Multiscale Models
N Panda, T Butler, D Estep, L Graham, C Dawson
International Journal for Multiscale Computational Engineering 15 (3), 2017
Mandates: US National Science Foundation, US Department of Energy
Flyer Plate Continuum Simulations Informed with Machine Learning Crack Evolution
MG Fernandez-Godino, N Panda, D O'Malley, KS Hickmann, DA Oyen, ...
AIAA Scitech 2020 Forum, 1410, 2020
Mandates: US Department of Energy
Available somewhere: 10
StressNet-Deep learning to predict stress with fracture propagation in brittle materials
Y Wang, D Oyen, W Guo, A Mehta, CB Scott, N Panda, ...
Npj Materials Degradation 5 (1), 6, 2021
Mandates: US National Science Foundation, US Department of Energy
Surrogate models for estimating failure in brittle and quasi-brittle materials
MK Mudunuru, N Panda, S Karra, G Srinivasan, VT Chau, E Rougier, ...
Applied Sciences 9 (13), 2706, 2019
Mandates: US Department of Energy
Mesoscale informed parameter estimation through machine learning: A case-study in fracture modeling
N Panda, D Osthus, G Srinivasan, D O'Malley, V Chau, D Oyen, ...
Journal of Computational Physics 420, 109719, 2020
Mandates: US Department of Energy
What is the gradient of a scalar function of a symmetric matrix?
S Srinivasan, N Panda
Indian Journal of Pure and Applied Mathematics 54 (3), 907-919, 2023
Mandates: US Department of Energy
Accelerating high-strain continuum-scale brittle fracture simulations with machine learning
MG Fernández-Godino, N Panda, D O’Malley, K Larkin, A Hunter, ...
Computational Materials Science 186, 109959, 2021
Mandates: US Department of Energy
A data-driven non-linear assimilation framework with neural networks
N Panda, MG Fernández-Godino, HC Godinez, C Dawson
Computational Geosciences 25, 233-242, 2021
Mandates: US Department of Energy
Physics-Informed Spatiotemporal Deep Learning for Emulating Coupled Dynamical Systems.
A Mehta, CB Scott, D Oyen, N Panda, G Srinivasan
AAAI Spring Symposium: MLPS, 2020
Mandates: US Department of Energy
Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks
D Banesh, N Panda, A Biswas, L Van Roekel, D Oyen, N Urban, ...
2021 IEEE International Conference on Big Data (Big Data), 3731-3739, 2021
Mandates: US Department of Energy
Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]
K Kontolati, N Panda, NE Klein, JS Moore, DA Oyen
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2021
Mandates: US Department of Energy
A Multi-Scale Inference, Estimation, and Prediction Engine for Earth System Modeling
M Anghel, B Nadiga, N Panda, A Mohan, E Hunke, C Begeman
Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2021
Mandates: US Department of Energy
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