P. Balaprakash

发表

Application of machine learning and artificial intelligence to extend EFIT equilibrium reconstruction

L. Lao, S. Kruger, J. Leddy, 2022, Plasma Physics and Controlled Fusion.

Sequential Bayesian Neural Subnetwork Ensembles

Sanket R. Jantre, T. Maiti, Shrijita Bhattacharya, 2022, ArXiv.

Multifidelity Reinforcement Learning with Control Variates

P. Balaprakash, Sami Khairy, 2022, 2206.05165.

Asynchronous Distributed Bayesian Optimization at HPC Scale

I. Guyon, V. Vishwanath, P. Balaprakash, 2022, ArXiv.

Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling

R. Maulik, Q. Tang, N. Garland, 2021, 2112.05325.

Multifidelity Reinforcement Learning with Control Variates

P. Balaprakash, Sami Khairy, 2022, ESANN 2023 proceesdings.

Efficient training of artificial neural network surrogates for a collisional-radiative model through adaptive parameter space sampling

R. Maulik, Q. Tang, N. Garland, 2021, 2112.05325.

Stabilized Neural Ordinary Differential Equations for Long-Time Forecasting of Dynamical Systems

Alec J. Linot, M. Graham, R. Maulik, 2022, J. Comput. Phys..

Efficient data acquisition and training of collisional-radiative model artificial neural network surrogates through adaptive parameter space sampling

R. Maulik, Xianzhu Tang, Q. Tang, 2022, Mach. Learn. Sci. Technol..

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

A. Obabko, Dezhi Dai, P. Balaprakash, 2021 .

Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness

P. Balaprakash, S. Madireddy, Sumegha Premchandar, 2022, 2210.04083.

Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting

Tanwi Mallick, H. Meidani, Weiheng Zhong, 2022, ArXiv.

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

R. Harder, M. Cherukara, S. Sankaranarayanan, 2021, npj Computational Materials.

Multi-fidelity reinforcement learning framework for shape optimization

R. Maulik, S. Pawar, Sahil Bhola, 2022, Journal of Computational Physics.

AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Gianmarco Mengaldo, Romit Maulik, Emil Constantinescu, 2021, Geoscientific Model Development.

Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck

P. Balaprakash, S. Madireddy, Anirban Samaddar, 2022, AISTATS.

AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

R. Maulik, E. Constantinescu, I. Foster, 2022 .

Modeling design and control problems involving neural network surrogates pdf

Sven Leyffer, Prasanna Balaprakash, Dominic Yang, 2021, Computational Optimization and Applications.

Multi-fidelity reinforcement learning framework for shape optimization

R. Maulik, S. Pawar, Sahil Bhola, 2022, J. Comput. Phys..

Continual Learning via Dynamic Programming

P. Balaprakash, R. Krishnan, 2022, 2022 26th International Conference on Pattern Recognition (ICPR).

Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles

R. Maulik, Krishnan Raghavan, P. Balaprakash, 2023, Physica D: Nonlinear Phenomena.

Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting

Tanwi Mallick, P. Balaprakash, Jane Macfarlane, 2022, ArXiv.

Transfer-learning-based Autotuning using Gaussian Copula

P. Hovland, Xingfu Wu, B. Videau, 2023, International Conference on Supercomputing.

Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach

Tanwi Mallick, Joshua Bergerson, J. Hutchison, 2023, ArXiv.

Application of probabilistic modeling and automated machine learning framework for high-dimensional stress field

M. Anitescu, Piyush Pandita, Changjie Sun, 2023, arXiv.org.

Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives

V. Vishwanath, P. Balaprakash, Romain Egele, 2023, ArXiv.

Self-supervised Learning for Anomaly Detection in Computational Workflows

A. Mandal, Hongwei Jin, Krishnan Raghavan, 2023, ArXiv.