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 for solving grand challenge problems in computational science. ISCL is housed in the Information Sciences and Technology Department at Pennsylvania State University, as well as the Mathematics and Computer Science Division at Argonne National Laboratory. Our team is composed of postdoctoral fellows, graduate students, and visiting students across these two locations (with opportunities to travel between them). A high level overview of our research may be found in the following talks: [1], [2], [3], [4], [5]. Further information about publications can be found on Google Scholar and our software contributions are available on Github. ISCL eagerly welcomes possibilities for collaboration! Feel free to reach out to us for any questions.
Members of our group have a unique platform for impactful research with access to Argonne's state-of-the-art supercomputing resources and the ability to work on large-scale research projects of strategic importance.
News
Our paper on the use of the Quantum Approximate Optimization Algorithm to build reduced-order models of fluid flows is published in the Journal of Computational Physics! This work was done in collaboration with Argonne National Laboratory and the University of Glasgow. Read more here.
Romit presents a guest lecture at Texas A&M's NUEN 689 course ("Deep Learning for Engineering Applications"). Thank you to Professor Yang Liu for the invitation!
Romit presents invited talks related to work on scientific machine learning at the HKUST Mechanical Engineering Seminar Series and the UNRAVEL project in Netherlands (thank you to Prof. Lin Fu and Benjamin Sanderse for the invitation!)
Two papers accepted as posters at the ICLR 2024 AI4DiffEqtnsInSci workshop! The first introduces a scalable vision transformer model for weather forecasting (Stormer) and the second studies the preservation of time-scales in the latent space of autoencoder reduced-order models. Official links to be posted shortly.