303 citations · 2.2k across the 86 of their papers we have counts for
14 papers · 1 filter
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
Tung Nguyen, Rohan Shah, Hritik Bansal +6
Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep l…
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Shivam Barwey, Hojin Kim, Romit Maulik
Data-driven surrogate modeling has surged in capability in recent years with the emergence of graph neural networks (GNNs), which can operate directly on mesh-based representations…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…
Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package
Marcin Rogowski, Brandon C. Y. Yeung, Oliver T. Schmidt +4
We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the d…
Robust experimental data assimilation for the Spalart-Allmaras turbulence model
Deepinder Jot Singh Aulakh, Xiang Yang, Romit Maulik
This study presents a methodology focusing on the use of computational model and experimental data fusion to improve the Spalart-Allmaras (SA) closure model for Reynolds-averaged N…
Generalizable data-driven turbulence closure modeling on unstructured grids with differentiable physics
Hojin Kim, Varun Shankar, Venkatasubramanian Viswanathan +1
Differentiable physical simulators are proving to be valuable tools for developing data-driven models for computational fluid dynamics (CFD). In particular, these simulators enable…
Importance of equivariant and invariant symmetries for fluid flow modeling
Varun Shankar, Shivam Barwey, Zico Kolter +2
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning pr…
Differentiable Turbulence: Closure as a partial differential equation constrained optimization
Varun Shankar, Dibyajyoti Chakraborty, Venkatasubramanian Viswanathan +1
Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage…
Reduced-order Modeling on a Near-term Quantum Computer
Katherine Asztalos, René Steijl, Romit Maulik
Quantum computing is an advancing area of research in which computer hardware and algorithms are developed to take advantage of quantum mechanical phenomena. In recent studies, qua…
Online data-driven changepoint detection for high-dimensional dynamical systems
Sen Lin, Gianmarco Mengaldo, Romit Maulik
The detection of anomalies or transitions in complex dynamical systems is of critical importance to various applications. In this study, we propose the use of machine learning to d…
Generative modeling of time-dependent densities via optimal transport and projection pursuit
Jonah Botvinick-Greenhouse, Yunan Yang, Romit Maulik
Motivated by the computational difficulties incurred by popular deep learning algorithms for the generative modeling of temporal densities, we propose a cheap alternative which req…
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles
Romit Maulik, Romain Egele, Krishnan Raghavan +1
Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DN…
Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning
Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan +1
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here…
Physics-Informed Neural Networks for Mesh Deformation with Exact Boundary Enforcement
Atakan Aygun, Romit Maulik, Ali Karakus
In this work, we have applied physics-informed neural networks (PINN) for solving mesh deformation problems. We used the collocation PINN method to capture the new positions of the…