303 citations · 2.2k across the 86 of their papers we have counts for
93 papers
Puncture Evolution with Physics-Informed Neural Networks
Estuti Shukla, Ashwin Suriyanarayanan, David Radice +2
We investigate the application of physics-informed neural networks (PINNs) to solve the vacuum Einstein equations in spherical symmetry. Our neural network architecture is designed…
Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces
Yang Xu, Dibyajyoti Chakraborty, Haiwen Guan +2
Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate sta…
Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale
Hong Zhang, John K. Hutchison, Rao Kotamarthi +8
Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. A central challenge is to produce high-resoluti…
Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning
Ashwin Suriyanarayanan, Romit Maulik
Rotating detonation engines (RDEs) exhibit strongly nonlinear, multiscale wave dynamics that set the observed thermal field. High-fidelity simulations (DNS/LES) resolve these struc…
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
Dibyajyoti Chakraborty, Romit Maulik
Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data. In this study, we propose a fundamentally different, unified ap…
Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses
Kanad Sen, Romit Maulik
Surrogate modeling for high-dimensional nonlinear dynamical systems that exhibit chaos requires mechanisms that preserve not only pointwise accuracy but also the scale-dependent st…
Multiscale Hypersonic Boundary Layer Reconstruction via Spectral Binning and Subdomain-wise Conditional Diffusion
Hojin Kim, Dibyajyoti Chakraborty, Takahiko Toki +2
We propose a multiscale probabilistic reconstruction framework for hypersonic Couette flow, where near-wall states are inferred from limited top-wall observations using conditional…
Deep Learning of Solver-Aware Turbulence Closures from Nudged LES Dynamics
Ashwin Suriyanarayanan, Dibyajyoti Chakraborty, Romit Maulik
The differentiable physics paradigm may be leveraged as an a-posteriori approach for discovering turbulence closure models by embedding a neural network parameterization directly i…
Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systems
Dibyajyoti Chakraborty, Hojin Kim, Romit Maulik
High-fidelity numerical simulations of chaotic, high dimensional nonlinear dynamical systems are computationally expensive, necessitating the development of efficient surrogate mod…
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
Haiwen Guan, Dibyajyoti Chakraborty, Moein Darman +3
The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-rang…
SC3D: Dynamic and Differentiable Causal Discovery for Temporal and Instantaneous Graphs
Sourajit Das, Dibyajyoti Chakraborty, Romit Maulik
Discovering causal structures from multivariate time series is a key problem because interactions span across multiple lags and possibly involve instantaneous dependencies. Additio…
A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
Xuyang Li, John Harlim, Dibyajyoti Chakraborty +1
The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and engineering disciplines. A sign…
Interpretable Diagnostics and Adaptive Data Assimilation for Neural ODEs via Discrete Empirical Interpolation
Hojin Kim, Romit Maulik
We present a framework that leverages the Discrete Empirical Interpolation Method (DEIM) for interpretable deep learning and dynamical system analysis. Although DEIM efficiently ap…
MoWE : A Mixture of Weather Experts
Dibyajyoti Chakraborty, Romit Maulik, Peter Harrington +3
Data-driven weather models have recently achieved state-of-the-art performance, yet progress has plateaued in recent years. This paper introduces a Mixture of Experts (MoWE) approa…
Localized PCA-Net Neural Operators for Scalable Solution Reconstruction of Elliptic PDEs
Mrigank Dhingra, Romit Maulik, Adil Rasheed +1
Neural operator learning has emerged as a powerful approach for solving partial differential equations (PDEs) in a data-driven manner. However, applying principal component analysi…
LUCIE-3D: A three-dimensional climate emulator for forced responses
Haiwen Guan, Troy Arcomano, Ashesh Chattopadhyay +1
We introduce LUCIE-3D, a lightweight three-dimensional climate emulator designed to capture the vertical structure of the atmosphere, respond to climate change forcings, and mainta…
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
Riddhiman Raut, Romit Maulik, Shivam Barwey
This work presents a novel graph neural network (GNN) architecture, the Feature-specific Interpretable Graph Neural Network (FIGNN), designed to enhance the interpretability of dee…
Multimodal Atmospheric Super-Resolution With Deep Generative Models
Dibyajyoti Chakraborty, Haiwen Guan, Jason Stock +3
Score-based diffusion modeling is a generative machine learning algorithm that can be used to sample from complex distributions. They achieve this by learning a score function, i.e…
SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning
Xuyang Li, Romit Maulik
Modern deep reinforcement learning (DRL) methods have made significant advances in handling continuous action spaces. However, real-world control systems, especially those requirin…
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints
Xin Wang, Jianda Chen, Juntao Yang +8
Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate. However, current general circulation models (GCMs) face challenges in capturing u…
Binned Spectral Power Loss for Improved Prediction of Chaotic Systems
Dibyajyoti Chakraborty, Arvind T. Mohan, Romit Maulik
Forecasting multiscale chaotic dynamical systems, such as turbulent flows, with deep learning remains a formidable challenge due to the spectral bias of neural networks, which hind…
Semi-Implicit Neural Ordinary Differential Equations
Hong Zhang, Ying Liu, Romit Maulik
Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in…
Improved deep learning of chaotic dynamical systems with multistep penalty losses
Dibyajyoti Chakraborty, Seung Whan Chung, Ashesh Chattopadhyay +1
Predicting the long-term behavior of chaotic systems remains a formidable challenge due to their extreme sensitivity to initial conditions and the inherent limitations of tradition…
Scalable and Consistent Graph Neural Networks for Distributed Mesh-based Data-driven Modeling
Shivam Barwey, Riccardo Balin, Bethany Lusch +5
This work develops a distributed graph neural network (GNN) methodology for mesh-based modeling applications using a consistent neural message passing layer. As the name implies, t…