Publications

Journal Articles

  1. Thomas O’Leary‐Roseberry, Peng Chen, Umberto Villa, and Omar Ghattas. Derivative Informed Neural Operator: An Efficient Framework for High‐Dimensional Parametric Derivative Learning, Journal of Computational Physics, Volume 496, 112555, Published, 2024, DOI: https://doi.org/10.1016/j.jcp.2023.112555.
  2. N. Alger, T. Hartland, N. Petra, and O. Ghattas. Point spread function approximation of high rank Hessians with locally supported non‐negative integral kernels, SIAM Journal on Scientific Computing, Volume 46, A1658, Published, 2024, DOI: https://doi.org/10.1137/23M1584745.
  3. Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden. Bayesian model calibration for block copolymer self‐assembly: Likelihood‐free inference and expected information gain computation via measure transport, Journal of Computational Physics, Volume 503, 112844, Published, 2024, DOI: https://doi.org/10.1016/j.jcp.2024.112844.
  4. Simone Puel, Thorsten W. Becker, Umberto Villa, Omar Ghattas, Dunyu Liu. Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku‐oki M9, Science Advances, Volume 10, eadl4264, Published, 2024, DOI: https://doi.org/10.1126/sciadv.adl4264.
  5. Lianghao Cao, Thomas O’Leary‐Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas. Residual‐Based Error Correction for Neural Operator Accelerated Infinite‐Dimensional Bayesian Inverse Problems, Journal of Computational Physics, Volume 486, 112104, Published, 2023, DOI: https://doi.org/10.1016/j.jcp.2023.112104.
  6. Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, Omar Ghattas. Bayesian Model Calibration for Diblock Copolymer Thin Film Self‐Assembly Using Power Spectrum of Microscopy Data and Machine Learning, Computer Methods in Applied Mechanics and Engineering, 116349, Published, 2023, DOI: https://doi.org/10.1016/j.cma.2023.116349.
  7. Dingcheng Luo, Peng Chen, Thomas O’Leary‐Roseberry, Umberto Villa, and Omar Ghattas. SOUPy: Stochastic PDEconstrained optimization under high‐dimensional uncertainty in Python, Journal of Open Source Software, Submitted.
  8. Sapienza, F., Bolibar, J., Schäfer, F., Groenke, B., Pal, A., Boussange, V., Heimbach, P., Hooker, G., Pérez, F., Persson, P.‐O. & Rackauckas, C.. Differentiable Programming for Differential Equations: A Review, Submitted, Submitted, DOI: https://doi.org/10.48550/arxiv.2406.09699.
  9. Bassel Saleh, Aaron Zimmerman, Peng Chen, Omar Ghattas. Tempered Multifidelity Importance Sampling for Gravitational Wave Parameter Estimation, Physical Review D, Submitted.
  10. Sreeram Venkat, Milinda Fernando, Stefan Henneking, and Omar Ghattas. Fast and Scalable FFT‐Based GPU‐Accelerated Algorithms for Hessian Actions Arising in Linear Inverse Problems Governed by Autonomous Dynamical Systems, SIAM Journal for Scientific Computing, Other.
  11. Julie V. Pham, Omar Ghattas, Noel T. Clemens, and Karen E. Willcox. Real‐time aerodynamic load estimation for hypersonics via strain‐based inverse maps, AIAA Journal, Submitted.
  12. Lianghao Cao, Thomas O’Leary‐Roseberry, and Omar Ghattas. Derivative‐informed neural operator acceleration of geometric MCMC for infinite‐dimensional Bayesian inverse problems, Journal of Machine Learning Research, Submitted, arXiv: https://arxiv.org/abs/2403.08220.
  13. D. Luo, T. O’Leary‐Roseberry, P. Chen, and O. Ghattas. Efficient PDE‐constrained optimization under high‐dimensional uncertainty using derivative‐informed neural operators, SIAM Journal on Scientific Computing, Submitted.

Conference Papers and Presentations

  1. Omar Ghattas. Towards Real Time Bayesian Inversion and Prediction of Megathrust Tsunamis, 22nd Computational Fluids Conference (CFC2023), This was a presentation, 2028.
  2. Omar Ghattas. Geometric neural operators for scalable digital twins, 22nd Computational Fluids Conference (CFC2023), This was a presentation, 2028.
  3. Aretz, N. and Willcox, K. Enforcing structure in data‐driven reduced modeling through nested Operator Inference, Conference on Decision and Control (CDC24), Accepted, 2024.
  4. Omar Ghattas. A Digital Twin for Real Time Bayesian Inference and Prediction of Tsunamis, CIRM Workshop on Digital Twins for Inverse Problems in Earth Science, Published, 2024.
  5. Karen Willcox. From reduced‐order modeling to scientific machine learning, SIAM Annual Meeting, Published, 2024.
  6. Karen Willcox Mathematical and Computational Foundations for Predictive Digital Twins at Scale, World Congress on Computational Mechanics, Published, 2024.
  7. Omar Ghattas. Geometric Neural Operators for Bayesian Inverse Problems and Optimization Under Uncertainty, 9th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS), Published, 2024.
  8. Karen Willcox. Redefining Engineering Simulation and Design in the Age of AI: Fact or Fiction?, SFI Symposium on the Past and Future of Complexity Science, Published, 2024.
  9. Karen Willcox. Enforcing structure in scientific machine learning: The role of projection‐based reduced‐order modeling, GAMM Annual Meeting, Published, 2024.
  10. Omar Ghattas. Geometric Deep Neural Operators for Bayesian Inverse Problems, SIAM Conference on Uncertainty Quantification, Published, 2024.
  11. Karen Willcox. Future Directions for Digital Twins, NAFEMS ASSESS, Published, 2024.
  12. Karen Willcox. Future Directions for Digital Twins, Multicore World, Published, 2024.
  13. Youguang Chen and George Biros. FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression, NeurIPS,Published, 2023.
  14. Omar Ghattas. Geometric neural operators for digital twins, Mathematical Opportunities in Digital Twins Workshop (MATH‐DT), Published, 2023.
  15. Karen Willcox. Reduced‐Order Models as Enablers for Design, Control and Predictive Digital Twins, MORTech conference, Published, 2023.
  16. Omar Ghattas. Digital twins for natural hazards, Workshop on Crosscutting Research Needs for Digital Twins, Published, 2023.
  17. Omar Ghattas. Geometric neural operators for inverse and optimal control problems governed by PDEs, Advanced in Computational Mechanics (ACM23), Published, 2023.
  18. Karen Willcox. Learning physics‐based models from data: Perspectives from projection‐based model reduction, Advanced in Computational Mechanics (ACM23), Published, 2023.
  19. Omar Ghattas. Geometric neural surrogates for optimization problems governed by PDEs (invited plenary), Modeling and Optimization: Theory and Applications (MOPTA) 2023,, This was a presentation, 2023.
  20. Karen Willcox. Nonlinear manifold approximations for reduced‐order modeling of nonlinear systems (invited semi‐plenary), Workshop on Mathematical Foundations of Data Assimilation and Inverse Problems, Foundations of Computational Mathematics, This was a presentation, 2023.
  21. George Biros. SciML models for microstructure texture prediction, SimTech colloquium, University of Stuttgart, This was a presentation, 2023.
  22. Karen Willcox. Digital Twins, CTO Forum, This was a presentation, 2023.
  23. Karen Willcox. Reduced‐Order Models as Enablers for Design, Control and Predictive Digital Twins, AFRL/AFOSR Chief Scientist Distinguished Lecture, This was a presentation, 2023.
  24. Karen Willcox. Digital Twins: How high‐performance computing is personalizing the future for complex systems, Supercomputing Spotlights Webinar Series, This was a presentation, 2023.
  25. Karen Willcox. Learning physics‐based models from data: Perspectives from projection‐based model reduction, MIT Distinguished Seminar Series in Computational Science and Engineering, This was a presentation, 2023.
  26. Omar Ghattas. Panel on Building Your Research Brand, 2023 Rising Stars in Computational and Data Science, This was a presentation, 2023.
  27. Omar Ghattas. Neural surrogates for digital twins, Workshop on Scientific Machine Learning (SciML), This was a presentation, 2023.
  28. Omar Ghattas. Neural surrogates for digital twins, Workshop on Modelling, Analysis and Inference for Digital Twins, Program on Mathematical and Statistical Foundations of Future Data‐Driven Engineering, Isaac Newton Institute for Mathematical Sciences, This was a presentation, 2023.
  29. George Biros. Deep Reduced Order Models for Grain Microstructure Evolution, SIAM Conference on Computational Science and Engineering, This was a presentation, 2023.
  30. Omar Ghattas. Parsimonious deep neural network surrogates for PDE‐constrained Bayesian optimal experimental design, SIAM Conference on Computational Science and Engineering, This was a presentation, 2023.
  31. Karen Willcox. Predictive Digital Twins at Scale (Opening Keynote Lecture), Multicore World, This was a presentation, 2023.
  32. Karen Willcox. Digital Twins (Community Lecture ‐ invited plenary session), SIAM Conference on Computational Science and Engineering, This was a presentation, 2023.
  33. Omar Ghattas. Deep neural surrogates for digital twins, Workshop on Digital Twins in Atmospheric, Climate, and Sustainability Science, National Academies of Sciences, Engineering, and Medicine, This was a presentation, 2023.
  34. Karen Willcox. Forum 360: Addressing Increasing Complexity in Aerospace Systems (invited panel), AIAA SciTech Forum & Exhibition, This was a presentation, 2023.
  35. Karen Willcox. Beyond forward simulations: From reduced‐order models to digital twins with computational science (invited plenary), 5th Annual Meeting of the SIAM Texas‐Louisiana Section, This was a presentation, 2022.
  36. Karen Willcox. Predictive Digital Twins: From Aerospace Engineering to Computational Oncology (invited plenary talk), Computational Techniques and Applications Conference (CTAC), This was a presentation, 2022.
  37. Omar Ghattas. Geometric Deep Neural Network Surrogates for Bayesian Inverse Problems, Third Symposium on Knowledge‐Guided ML (KGML‐AAAI‐22), AAAI 2022 Fall Symposium Series, This was a presentation, 2022.
  38. Omar Ghattas. Towards real time Bayesian inversion and prediction of megathrust tsunamis, Oberwolfach Workshop on Mathematical Advances in Geophysical Fluid Dynamics, This was a presentation, 2022.
  39. Omar Ghattas. A Perspective on Foundational Research Gaps and Future Directions for Predictive Digital Twins, National Academies Committee on Foundational Research Gaps and Future Directions for Digital Twins, This was a presentation, 2022.
  40. Karen Willcox, Mathematical and Computational Foundations for Enabling Predictive Digital Twins at Scale (invited Colloquium), 2022 Lecons Jacques‐Louis Lion, This was a presentation, 2022.
  41. Karen Willcox, Digital Twins. The personalized future of computing for complex systems, Society of Women Engineers, Central Illinois Chapter, This was a presentation, 2022.
  42. George Biros. GrainNN: An LSTM for predicting microstructure evolution during polycrystalline grain formation, Workshop on Scientific Machine Learning for Complex Systems: Beyond Forward Simulation to Inference and Optimization, This was a presentation, 2022.
  43. Omar Ghattas. Geometric Deep Neural Network Surrogates for Bayesian Inverse Problems (invited plenary), Peruvian Conference on Scientific Computing, This was a presentation, 2022.
  44. Omar Ghattas. Structure‐exploiting deep neural networks for PDE‐governed Bayesian inversion and optimal experimental design, SIAM Conference on Mathematics of Data Science, This was a presentation, 2022.
  45. Karen Willcox. Beyond forward simulations: From reduced‐order models to digital twins with computational science (keynote), Sayas Numerics Day, This was a presentation, 2022.
  46. Karen Willcox. Beyond forward simulations: From reduced‐order models to digital twins with computational science (opening keynote), Congress on Numerical Methods in Engineering, This was a presentation, 2022.