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 Science 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]. 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

Our paper on using Bayesian online changepoint detection to detect anomalies in complex dynamical systems is published in Chaos! This work was led by Sen Lin (at the University of Houston) in collaboration with Professor Gianmarco Mengaldo at the National University of Singapore. Read more here.
Our paper on using projection pursuit optimal transport for learning stochastic dynamical systems is published in Chaos! This work was led by Jonah Botvinick-Greenhouse in collaboration with Professor Yunan Yang at Cornell. Read more here.
ISCL is funded by the U.S. Department of Energy for developing novel scientific machine learning algorithms for Tokamak Disruption mitigation. More information here here.
Neelappagouda (Neel) and Dibyajyoti (DJ) are the newest graduate students at ISCL-Penn State. They will be working on scalable unstructured scientific machine learning for dynamical systems. Welcome!
Romit will be visiting George Mason University to provide a seminar on the 27th of October. Thank you Sean Carney and Harbir Antil for the kind invitation!
Romit will be visiting the CWI Autumn School on Scientific Machine Learning in Amsterdam between October 9 to October 13. A big thank you to Benjamin Sanderse for the invitation!
Varun Shankar defends his thesis at Carnegie Mellon! ISCL is grateful to have him and Dr. Vishwanathan as a collaborator.
See other archived news here.

Research information

The research manifesto of the interdisciplinary scientific computing group.

Grand challenge problems

Improving geophysical forecast models with data science. We are building models to forecast variables using scientific machine learning. (credit Troy Arcomano - collaborator).

Tokamak disruption mitigation for nuclear fusion (image taken from Boozer et al., 2012). We are building closures and surrogate models to accelerate the computation of complex nuclear fusion reactors (i.e., system level simulations).

Scientific ML algorithm development

An algorithm that builds deep learning function approximation for sparse, unstructured, and time-varying observations (Nature Machine Intelligence 3 (11), 945-951, 2021).

A novel graph neural network architecture that not only makes predictions but also identifies salient physical features in an interpretable latent space (credit Shivam Barwey - postdoc).

Applied machine learning

Building a wind-turbine wake model for on-shore wind farms in the Texas panhandle using LIDAR and meteorological data (Neural Computing and Applications 34 (8), 6171-6186, 2022).

Characterizing the schooling of fish using generative machine learning. Here, the variation in the density of fish is captured by an optimal transport based forecast model (credit Jonah Botvinick-Greenhouse - visiting graduate student).

High performance heterogeneous computing

Building a scalable and reproducible ecosystem for scientific machine learning research (Journal of Computational Science 62 (2022): 101750).