Ashesh Chattopadhyay

Intern/Affiliate, Lawrence Berkeley National Lab

Joined

September 2020

Names

Ashesh Chattopadhyay (Preferred)

Ashesh Kumar Chattopadhyay

Emails

****@rice.edu (Confirmed)

Personal Links

Homepage

Google Scholar

DBLP

Career & Education History

Intern/Affiliate

Lawrence Berkeley National Lab (lbl.gov)

2020 – Present

PhD student

Rice University (rice.edu)

2017 – 2022

Advisors, Relations & Conflicts

PhD Advisor

Pedram Hassanzadeh

2017 – Present

Expertise

theoretical deep learning

neural tangents

convergence and generalization

infinite networks

2017 – Present

physics informed deep learning

2017 – Present

deep learning for physical sciences

complex systems

and chaotic dynamics

2017 – Present

geometric deep learning

equivariance

symmetry preserving networks

2017 – Present

Publications

Explaining the physics of transfer learning in data-driven turbulence modeling
Adam Subel, Yifei Guan, Ashesh Chattopadhyay, pedram hassanzadeh
- OpenReview Archive Direct Upload
- Readers: Everyone
Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, Karthik Kashinath
- 2021 (modified: 12 May 2025)
- CoRR 2021
- Readers: Everyone
Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Karthik Kashinath
- 2020 (modified: 12 May 2025)
- CI 2020
- Readers: Everyone
Analog forecasting of extreme-causing weather patterns using deep learning
Ashesh Chattopadhyay, Ebrahim Nabizadeh, Pedram Hassanzadeh
- 2019 (modified: 12 May 2025)
- CoRR 2019
- Readers: Everyone
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Ashesh Chattopadhyay, Pedram Hassanzadeh, Krishna V. Palem, Devika Subramanian
- 2019 (modified: 12 May 2025)
- CoRR 2019
- Readers: Everyone
A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns
Ashesh Chattopadhyay, Pedram Hassanzadeh, Saba Pasha
- 2018 (modified: 12 May 2025)
- CoRR 2018
- Readers: Everyone
A framework to integrate MFiX with Trilinos for high fidelity fluidized bed computations
V. M. Krushnarao Kotteda, Ashesh Chattopadhyay, Vinod Kumar, William F. Spotz
- 2016 (modified: 02 Nov 2022)
- HPEC 2016
- Readers: Everyone

Ashesh Chattopadhyay

Names

Emails

Personal Links

Career & Education History

Advisors, Relations & Conflicts

Expertise

Publications

Explaining the physics of transfer learning in data-driven turbulence modeling

Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence

Analog forecasting of extreme-causing weather patterns using deep learning

Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM

A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns

A framework to integrate MFiX with Trilinos for high fidelity fluidized bed computations

Co-Authors