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 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

Pleased to announce a new paper with Andrew Gillette and Tyler Chang published in the Journal of Computational Physics. We construct scientific machine learning models in the latent space of autoencoders which provide error bounds (in that space). Read more
Congrats to Haiwen Guan for a big shout-out from ECMWF's Matthew Chantry! His project LUCIE continues to push predictive data science for the Earth System with an emphasis on compute and data requirements.
Dibyajyoti was awarded both the best paper and the best poster at CASML IISC! Congrats DJ!
Our postdoc Dr. Xuyang Li won the best short-talk at the Penn State postdoctoral symposium! Congrats Xuyang!
ISCL is awarded a new AIST ESDT project by NASA! We will develop AI surrogate-based data assimilation algorithms for NASA GEOS in collaboration with NASA Goddard, the University of Chicago, and Argonne National Laboratory. Read more about our project here.
Our work on developing models for the surface height and velocities of the Gulf of Mexico using neural ordinary differential equations is accepted to CASML at IISC Bangalore. This work has been led by Dibyajyoti and is also accepted as spotlight (<5%)! Learn more here.
Pleased to announce a new project with Prof. David Radice in the Department of Physics at Penn State supported by the Kaufman Foundation. We will study Black Hole tomography with machine learning-based inversion algorithms. Learn more here: here.
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).