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 multifidelity PINNs is published in CMAME! See a preprint of this paper here. Thank you to Dr. Sunwoong Yang and ISCL member Hojin Kim for their work on this collaboration.
Congratulations to Dibyajyoti Chakraborty and Haiwen Guan for securing visiting researcher positions at Los Alamos National Laboratory and Argonne National Laboratory! They will work on collaborations between ISCL and these institutions for neural differential equations and climate model emulation research respectively.
A collaboration with Prof. Aditya Grover (UCLA) and (Rao Kotamarthi) Argonne National Laboratory leads to a best paper at the ICLR 2024 Climate Change for AI workshop! See a preprint of this paper here. We introduce a novel vision transformer based emulator for the atmosphere.
A new paper published in collaboration with Gianmarco Mengaldo (NUS), Oliver Schmidt (UCSD), Marcin Rogowski, Matteo Parsani, and Lisandro Dalcin (KAUST) is published in Computer Physics Communications. Read our preprint here.
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.