3 citations · 5 across the 13 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
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…
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…
A note on the error analysis of data-driven closure models for large eddy simulations of turbulence
Dibyajyoti Chakraborty, Shivam Barwey, Hong Zhang +1
In this work, we provide a mathematical formulation for error propagation in flow trajectory prediction using data-driven turbulence closure modeling. Under the assumption that the…
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…