News

• September 25, 2024
  Our paper Derivative-enhanced Deep Operator Network is accepted at NeurIPS 2024. Congratulations Yuan and Nolan!
• September 13, 2024
  Our paper Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator is submitted. Congratulations Jinwoo!
• August 29, 2024
  Our paper Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data is submitted. Congratulations Phillip!
• August 19-21, 2024
  Peng presents a talk at Workshop on Data-driven PDE-based inverse problem, in theory and practice, UW Madison on Neural Surrogates for Fast and Scalable Bayesian Optimal Experimental Design. Thanks Prof. Qin Li for the invitation.
• August 15, 2024
  Benjamin Burns, Wenbo Hao, and Yanjie Tong join us as PhD students. Welcome to all!
• August 13, 2024
  Our paper Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs is submitted. Congratulations Dingcheng and all!
• August 5, 2024
  Congratulations to Jinwoo for successfully defending his PhD Thesis on Efficient and Scalable Machine Learning Methods for Robust Bayesian Optimal Experimental Design!
• July 21-26, 2024
  Peng presents a talk at World Congress on Computational Mechanics 2024, Vancouver on Derivative-enhanced deep operator networks. Peng also co-organizes a minisymposium on UQ and SciML with Dr. Danial Faghihi et. al.
• May 29, 2024
  A collaborated paper Tempered Multifidelity Importance Sampling for Gravitational Wave Parameter Estimation is submitted. Congratulations Bassel and all!
• April 6, 2024
  Peng presents a plenary talk at Southeast Applied and Computational Math Student Workshop, 2024 on Derivative-informed Neural Operators for PDE-Constrained Optimization under Uncertainty. Yuan presents a poster on Derivative-enhanced Deep Operator Networks.
• February 29, 2024
  Our paper Derivative-enhanced deep operator network is submitted. Congratulations Yuan and Nolan!
• February 28, 2024
  Our paper Probabilistic Bayesian optimal experimental design using conditional normalizing flows is submitted. Congratulations Rafael Orozco! This is a joint work with Prof. Felix Herrmann at GT-CSE.
• February 27-March 1, 2024
  Peng has 8 co-authored talks at SIAM Conference on Uncertainty Quantification (UQ24). All PhD students present invited talks on their work, including Dingcheng on PDE-constrained optimization under uncertainty, Jinwoo on Bayesian optimal experimental design, Phillip on Semi-autoregressive energy flows, Sebastian on Control-DeepONet, and Yuan on derivative-enhanced DeepONet. Prof. Elizabeth Qian and Peng co-organize a minisymposium on Scalable Algorithms for Bayesian Inverse Problems.
• February 24, 2024
  Peng presents a plenary talk at Georgia Scientific Computing Symposium 2024 on Derivative-informed Neural Operators for PDE-Constrained Optimization under Uncertainty. Jinwoo, Phillip, Sebastian, and Yuan present four posters on Bayesian optimal experimental design, consistency model for data assimilation, Wasserstein gradient flows, and derivative-enhanced deep operator networks, respectively.
• February 1, 2024
  Peng speaks at Georgia Tech BBISS Seminar on Scientific Machine Learning for Predictive Digital Twins of Complex Systems.
• January 19, 2024
  Irene joins the group as a visiting Master student. Welcome Irene!
• January 16, 2024
  Peng co-organizes and presents at Georgia Tech Workshop on Foundation of Scientific AI for Optimization of Complex Systems.
• December 22, 2023
  Our paper Accelerating Bayesian Optimal Experimental Design with Derivative-Informed Neural Operators is submitted. Congratulations Jinwoo!
• December 21, 2023
  Phillip and Sebastian receive the Student Travel Awards to present at SIAM UQ24. Congratulations Phillip and Sebastian!
• December 19, 2023
  Peng speaks at 2023 Workshop on Scientific Computing and Large Data at University of South Carolina on Derivative-Informed Neural Operator for PDE-Constrained Optimization under Uncertainty. Thanks Prof. Wuchen Li for the invitation!
• December 4, 2023
  Nathan receives the President’s Undergraduate Research Award (PURA) to conduct research on variational inference. Congratulations Nathan!
• November 8, 2023
  Peng speaks at 2023 BSRA/SRNL AI Workshop on Scientific Machine Learning for Prediction, Inference, Experimental Design, and Control of Complex Systems.
• October 27, 2023
  Our paper Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning is published in Journal of Computational Physics. Congratulations Tom and all!
• October 25, 2023
  Peng speaks at Optimization in Oslo (OiO) Seminar on Derivative-Informed Neural Operator for PDE-Constrained Optimization under Uncertainty. Thanks Prof. Thomas M. Surowiec for the invitation!
• October 16, 2023
  Peng accepts an appointment as Brook Byers Institute for Sustainable Systems Faculty Fellow.
• October 3, 2023
  Peng receives an award of Thematic Events in AI from IDEaS to organize a workshop on Foundation of scientific AI (Artificial Intelligence) for Optimization of Complex Systems. Stay tuned.
• September 21-22, 2023
  Peng attends Oden Institute 50th Anniversary.
• September 12, 2023
  Peng speaks at Mechanical Engineering Seminar Series at University of Michigan. Thanks Prof. Xun Huan for the invitation!
• September 1, 2023
  Peng receives a Collaborative NSF Award (total $1.5M) on Variationally Stable Neural Networks for Simulation, Learning, and Experimental Design of Complex Physical Systems.
• August 25-26, 2023
  Peng speaks at Prof. Christoph Schwab 60th Birthday Workshop. Happy Birthday, Prof. Schwab!
• August 21-25, 2023
  Jinwoo and Peng speak at ICIAM 2023 Tokyo on Bayesian optimal experimental design and Bayesian inference, where Peng also co-organizes a minisymposium with Prof. Jinlong Wu on Scientific Machine Learning for Inverse Problems.
• August 15, 2023
  Phillip Si joins our group as a PhD student. Welcome Phillip!
  
• August 10, 2023
  A new version of our library SOUPy for stochastic optimization of complex systems under uncertainty is released. Congratulations and thanks to Dingcheng for leading this effort!
  
• August 6, 2023
  Peng presents a KDD'23 Tutorial on Uncertainty Quantification in Deep Learning. Thanks Lingkai for leading this tutorial!
• July 17, 2023
  Peng receives an NSF award as a CoPI on real-time flood prediction by data and model integration.
• June 30, 2023
  Peng speaks at DDPS Webinar, Lawrence Livermore National Laboratory on Derivative-informed neural operator. Thanks Dr. Siu Wun (Tony) Cheung for the invitation.
• June 21, 2023
  Jinwoo, Dingcheng, and Peng speak at Scientific Machine Learning, Banff International Research Station on Machine learning for Bayesian optimal experimental design, PDE-constrained optimization under uncertainty, and Bayesian inference problems. Thanks Dr. Thomas O'Leary-Roseberry for the invitation.
• June 8, 2023
  Our paper Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data is submitted. Congratulations Lianghao and all!
• June 1, 2023
  Peng presents at Mathematical and Scientific Machine Learning, ICERM on Derivative-informed neural operator for PDE-constrained optimization under uncertainty.
• June 1, 2023
  Peng speaks at SIAM Conference on Optimization (OP23) on Derivative-informed neural operator for PDE-constrained optimization under uncertainty.
• May 31, 2023
  Our paper Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators is submitted. Congratulations Dingcheng and all!
• April 28, 2023
  Peng speaks at Isaac Newton Institute on Projected variational inference for high-dimensional Bayesian inverse problems.
• April 21, 2023
  Peng speaks at Auburn University on Scalable Bayesian optimal experimental design for efficient data acquisition. Thanks Prof. Yimin Zhong for the invitation.
• March 27, 2023
  Our paper Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm is accepted for publication in Journal of Computational Physics. Congratulations Dingcheng and all!
• February 20, 2023
  Peng speaks at GaTech Math on Scalable Bayesian optimal experimental design for efficient data acquisition. Thanks Prof. Wenjing Liao for the invitation.
• February 2, 2023
  Our paper Large-scale Bayesian optimal experimental design with derivative-informed projected neural network is accepted for publication in Journal of Scientific Computing. Congratulations Keyi and all!
• January 27, 2023
  Our paper A greedy sensor selection algorithm for hyperparameterized linear Bayesian inverse problems is submitted. Congratulations Nicole and all!
• January 5, 2023
  James Reineke joins the group as an undergraduate student working on SciML for optimal experimental design. Welcome James!
• December 7, 2022
  Shikhar Jaiswal joins the group as a Master student working on SciML for stochastic optimization. Welcome Shikhar!
• November 30, 2022
  Peng joins the editorial board of Numerical Methods for Partial Differential Equations, an international journal that publishes the highest quality research in the rigorous analysis of novel techniques for the numerical solutions of partial differential equations (PDEs).
• November 10, 2022
  Peng speaks at Emory University in Atlanta. Thanks Prof. Mattias Chung for the invitation!
• November 8, 2022
  Nolan Bridge and Nathan Gong join the group as undergraduate students working on UQ for SciML. Welcome Nolan and Nathan!
• November 4-6, 2022
  Jinwoo attends and speaks at 5th Annual Meeting of the SIAM Texas-Louisiana Section in Houston.
• October 31, 2022
  Brian Zhang joins the group as an undergraduate student working on UQ and Bayesian inference. Welcome Brian!
• October 21, 2022
  Our paper A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. is accepted for publication in SIAM / ASA Journal on Uncertainty Quantification. Congratulations Keyi and all!
• September 26-October 2, 2022
  Peng attends and speaks at SIAM Conference on Mathematics of Data Science (MDS22) in San Diego. With Thomas O'Leary-Roseberry and Omar Ghattas, we organize a minisymposium on "Advances in Fast and Scalable Bayesian Inference".
• September 27, 2022
  Peng speaks at The University of Iowa in Iowa City. Thanks Prof. Xueyu Zhu for the invitation!
• September 9, 2022
  Our paper "An offline--online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement" is accepted for publication in SIAM Journal on Scientific Computing. Congratulations Keyi and all!
• September 6, 2022
  Our paper "Optimal design of photonic nanojets under uncertainty" is submitted. Congratulations Amal and all!
• August 22, 2022
  Yuan Qiu joins the group as a Master student working on SciML and neural operators. Welcome Yuan!
• August 18, 2022
  Yulong Liang joins the group as a Master student working on UQ for nuclear fusion. Welcome Yulong!
• August 13, 2022
  Jinwoo Go joins the group as a PhD student working on SciML for optimal experimental design. Welcome Jinwoo!
• August 2, 2022
  Our paper "Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm" is submitted. Congratulations Dingcheng and all!
• August 1, 2022
  Peng joins the School of CSE at Georgia Tech as an Assistant Professor.