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 also runs a scientific machine learning seminar series with leading researchers in our area of study - check it out here!
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
- Two new preprints led by Dibyajyoti Chakraborty and Haiwen Guan. In the former, we propose a state-of-the-art mixture of experts based AI-weather model. In the latter, we propose a three-dimensional neural operator based emulator of the atmosphere that is able to recover the planetary climatology, variability, and extremes.
- Romit presents a talk at the DR.SciML Conference at the University of Manchester. Thank you to Prof. Anirbit Mukherjee for the invitation.
- Romit will be presenting invited talks at three upcoming events covering a range of recent studies from ISCL. First, we will be at the Brin Mathematics Research Center for presenting lectures at the Summer School on Scientific Machine Learning (thank you Prof. Deep Ray!). Next, we will present at the JHU-IITD Joint Seminar Series on Scientific Machine Learning (thank you Prof. Souvik Chakraborty and Prof. Somdatta Goswami) and lastly we will also showcase our work at Texas A \& M University, Kingsville VISTA Lab seminar series (Thank you Prof. Arturo Rodriguez and Prof. Vinod Kumar).
- Pleased to announce a new preprint from our group, led by Dibyajyoti Chakraborty, on the use of generative modeling for zero-shot multimodal data assimilation for recovering the state of the state of the atmosphere from sparse data. By doing score-matching during inference, rapid state reconstructions are possible in seconds given observations from varying modalities. We are able to recover ERA5 reanalysis using partial observations of the same as well as sparse and unstructured observations from the IGRA radiosonde dataset. Read more on the Arxiv.
- Announcing a new preprint titled "FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models" where we extend previous work on interpretable subgraph extraction to feature-specific adaptivity. Our graph neural network models not only make predictions but also identify feature-specific spatially coherent subgraphs where a-posteriori errors are high. A preprint is available on the Arxiv.
- See other archived news here.