1 code implementation • 4 Apr 2024 • Tyler Chang, Andrew Gillette, Romit Maulik
In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models.
no code implementations • 5 Jan 2024 • Sunwoong Yang, Hojin Kim, Yoonpyo Hong, Kwanjung Yee, Romit Maulik, Namwoo Kang
This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives.
no code implementations • 6 Dec 2023 • Tung Nguyen, Rohan Shah, Hritik Bansal, Troy Arcomano, Sandeep Madireddy, Romit Maulik, Veerabhadra Kotamarthi, Ian Foster, Aditya Grover
At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals.
no code implementations • 13 Nov 2023 • Shivam Barwey, 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 of data.
no code implementations • 6 Oct 2023 • Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski, Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
no code implementations • 13 Sep 2023 • Deepinder Jot Singh Aulakh, Xiang Yang, Romit Maulik
This calibration relies on the assimilation of experimental data collected velocity profiles, skin friction, and pressure coefficients for separated flows.
no code implementations • 25 Jul 2023 • Varun Shankar, Romit Maulik, Venkatasubramanian Viswanathan
Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD).
no code implementations • 7 Jul 2023 • Varun Shankar, Dibyajyoti Chakraborty, Venkatasubramanian Viswanathan, Romit Maulik
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).
no code implementations • 3 May 2023 • Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik, Venkatasubramanian Viswanathan
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics.
no code implementations • 19 Apr 2023 • 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 requires minimal hyperparameter tuning and scales favorably to high dimensional problems.
no code implementations • 20 Feb 2023 • Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash
We demonstrate the feasibility of this framework for two tasks - forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature.
no code implementations • 13 Feb 2023 • Shivam Barwey, Varun Shankar, Venkatasubramanian Viswanathan, Romit Maulik
The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes.
no code implementations • 23 Sep 2022 • Varun Shankar, Vedant Puri, Ramesh Balakrishnan, Romit Maulik, Venkatasubramanian Viswanathan
Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences.
no code implementations • 29 Mar 2022 • Alec J. Linot, Joshua W. Burby, Qi Tang, Prasanna Balaprakash, Michael D. Graham, Romit Maulik
We present a data-driven modeling method that accurately captures shocks and chaotic dynamics by proposing a novel architecture, stabilized neural ordinary differential equation (ODE).
no code implementations • 22 Feb 2022 • Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
One key limitation of conventional DRL methods is their episode-hungry nature which proves to be a bottleneck for tasks which involve costly evaluations of a numerical forward model.
no code implementations • 26 Oct 2021 • Romain Egele, Romit Maulik, Krishnan Raghavan, Bethany Lusch, Isabelle Guyon, Prasanna Balaprakash
However, building ensembles of neural networks is a challenging task because, in addition to choosing the right neural architecture or hyperparameters for each member of the ensemble, there is an added cost of training each model.
no code implementations • 16 Sep 2021 • Masaki Morimoto, Kai Fukami, Romit Maulik, Ricardo Vinuesa, Koji Fukagata
The average of such an ensemble can be regarded as the `mean estimation', whereas its standard deviation can be used to construct `confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks.
no code implementations • 6 Sep 2021 • S. Ashwin Renganathan, Romit Maulik, Stefano Letizia, Giacomo Valerio Iungo
Physics-based models that capture the wake flow-field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced order models can represent an efficient alternative for simulating wind farms.
no code implementations • 29 Jul 2021 • Yubin Lu, Romit Maulik, Ting Gao, Felix Dietrich, Ioannis G. Kevrekidis, Jinqiao Duan
Specifically, the learned map is a multivariate normalizing flow that deforms the support of the reference density to the support of each and every density snapshot in time.
no code implementations • 13 Feb 2021 • Boumediene Hamzi, Romit Maulik, Houman Owhadi
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems.
1 code implementation • 3 Jan 2021 • Kai Fukami, Romit Maulik, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira
This reconstruction problem is especially difficult when sensors are sparsely positioned in a seemingly random or unorganized manner, which is often encountered in a range of scientific and engineering problems.
2 code implementations • 1 Dec 2020 • Romit Maulik, Himanshu Sharma, Saumil Patel, Bethany Lusch, Elise Jennings
We outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks.
no code implementations • 14 Nov 2020 • Dominic J. Skinner, Romit Maulik
Accurately forecasting the weather is a key requirement for climate change mitigation.
no code implementations • 23 Jul 2020 • Romit Maulik, Themistoklis Botsas, Nesar Ramachandra, Lachlan Robert Mason, Indranil Pan
We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.
1 code implementation • 8 May 2020 • Romit Maulik, Kai Fukami, Nesar Ramachandra, Koji Fukagata, Kunihiko Taira
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery.
Fluid Dynamics
no code implementations • 18 Sep 2019 • Romit Maulik, Rajeev Surendran Array, Prasanna Balaprakash
These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels).
no code implementations • 18 Sep 2019 • Romit Maulik, Vishwas Rao, Sandeep Madireddy, Bethany Lusch, Prasanna Balaprakash
Rapid simulations of advection-dominated problems are vital for multiple engineering and geophysical applications.