Welcome to the group page of the Interdisciplinary Scientific Computing Laboratory (ISCL)!. ISCL performs research at the intersection of data science, applied mathematics, and high-performance computing to enhance the understanding of complex multifidelity and multiphysics phenomena in various applications. In other words - we create science-based AI algorithms for applications such as weather and climate modeling, disruption mitigation in nuclear fusion, data and model fusion for complex fluid flows, surrogate models for chaotic dynamical systems, and more. ISCL also runs a scientific machine learning seminar series with leading researchers in our area of study - check it out here!
ISCL is housed in the College of Information Sciences and Technology at Pennsylvania State University, as well as the Mathematics and Computer Science Division at Argonne National Laboratory. A high level overview of our research may be found in the following talks: [1], [2], [3], [4], [5], [6]. Further information about publications can be found on Google Scholar and our software contributions are available on Github. ISCL has access to multiple HPC resources such as Bebop/Swing/Polaris (at Argonne), Roar (Penn State), and Perlmutter (NERSC). ISCL eagerly welcomes possibilities for education, collaboration, and consulting! Feel free to reach out to us for any questions.
News
- Congratulations to Dr. Xuyang Li for securing a Tenure-Track faculty position at UNC Charlotte! Also, congratulations to Dibyajyoti for an internship at Nvidia!
- Romit presents a Keynote talk at NCAR ISS 2025 related to DeepHyper! Learn more here.
- Two new publications by ISCL! Zach Malik leads a paper on data assimilation for chaotic systems (in CMAME) and Ashish Nair/Shivam Barwey lead a paper on the analysis of neural ordinary differential equations embedded in the latent space of autoencoders (in Physica D). Congratulations!
- Announcing a new preprint on interpretable reinforcement learning led by Xuyang Li. We identify a latent space for actions of an RL agent for linear stability analyses. Read more here.
- Announcing a new preprint on improved machine learning based predictions for chaotic dynamical systems led by Dibyajyoti Chakraborty. We simply use a novel loss formulation that can convert any ordinary neural architecture to one that can make improved predictions for chaotic multiscale systems such as turbulent flows. Read more here. This work was in collaboration with Arvind Mohan at Los Alamos National Laboratory.
- Pleased to announce a new paper with Varun Shankar, Dibyajyoti Chakraborty, and Venkat Vishwanathan in Physical Review Fluids. We demonstrate how differentiable programming leads to improved closures for the large eddy simulation of turbulence. Read more here.
- See other archived news here.