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.
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
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.
A new perspective piece of machine learning for wind energy is published in Theoretical and Applied Mechanics Letters. This work, titled "A Call for Enhanced Data-Driven Insights into Wind Energy Flow Physics", is collaborative with Professor Iungo and Coleman Moss at the University of Texas at Dallas.
New preprint out on a state-of-the-art transformer based model for weather forecasting! This work was collaborative with UCLA (Professor Aditya Grover). Read more here.
New preprint out on how we can construct graph neural networks for surrogate modeling with a-posteriori error tagging - a novel technique that improves the explainability of surrogate models! This work was led by Shivam Barwey. Read more here.