Talks and Presentations

Invited Talks

1. Physics-informed deep learning. Synced, Aug. 2021. (Video)
2. DeepONet: Learning nonlinear operators. University of Iowa, Department of Mathematics, May 2021.
3. Integrating machine learning & multiscale modeling. Purdue University, Department of Mathematics, Feb. 2021.
4. Integrating machine learning & multiscale modeling in biomedicine. Queen’s University, Department of Mechanical and Material Engineering, Feb. 2021. (Video)
5. Integrating machine learning & multiscale modeling in biomedicine. University of Pennsylvania, Department of Chemical and Biomolecular Engineering, Feb. 2021.
6. Physics-informed deep learning. Emory University, Scientific Computing Group, Apr. 2020.
7. Scientific machine learning. Lawrence Berkeley National Laboratory, Computing Sciences, Mar. 2020.
8. Scientific machine learning. Lawrence Livermore National Laboratory, Feb. 2020.
9. Scientific machine learning. Worcester Polytechnic Institute, Mathematical Sciences Department, Feb. 2020.
10. Scientific machine learning. Oak Ridge National Laboratory, Jan. 2020.
11. Scientific machine learning. Argonne National Laboratory, Mathematics and Computer Science Division, Jan. 2020.
12. Scientific machine learning. University of Pittsburgh, Department of Mechanical Engineering and Materials Science, Nov. 2019.
13. Scientific machine learning. University of North Carolina at Charlotte, Department of Mathematics and Statistics, Nov. 2019.
14. Collapse of deep and narrow neural nets. ICERM Scientific Machine Learning, Providence, RI, Jan. 2019. (Video)

Conference Presentations

1. DeepONet: Learning nonlinear operators. Conference on the Numerical Solution of Differential and Differential-Algebraic Equations, Martin Luther University Halle-Wittenberg, Germany, Sept. 2021.
2. DeepONet: Learning nonlinear operators. SIAM Conference on Applications of Dynamical Systems, Virtually, May 2021.
3. One-shot learning for solution operators of partial differential equations. ICLR Workshop on Deep Learning for Simulation, Virtually, May 2021.
4. DeepONet: Learning nonlinear operators based on the universal approximation theorem of operators. SIAM Conference on Computational Science and Engineering, Virtually, Mar. 2021.
5. DeepXDE: A deep learning library for solving differential equations. Workshop on Mathematical Machine Learning and Application, Pennsylvania State University, Virtually, Dec. 2020.
6. DeepONet: Learning nonlinear operators based on the universal approximation theorem of operators. SIAM Conference on Mathematics of Data Science, Virtually, June 2020. (Video)
7. DeepXDE: A deep learning library for solving differential equations. SIAM Conference on Mathematics of Data Science, Virtually, June 2020. (Video)
8. DeepXDE: A deep learning library for solving differential equations. AAAI Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physical Sciences, Stanford, CA, Mar. 2020. (Video)
9. DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. Joint Mathematics Meetings, Denver, CO, Jan. 2020.
10. DeepXDE: A deep learning library for solving forward and inverse differential equations. 3^rd Physics Informed Machine Learning Workshop, Santa Fe, NM, Jan. 2020. (Poster)
11. DeepXDE: A deep learning library for solving differential equations. Conference on Neural Information Processing Systems Workshop on Machine Learning and the Physical Sciences, Vancouver, Canada, Dec. 2019.
12. DeepXDE: A deep learning library for solving differential equations. Deep Learning for Science School, Berkeley, CA, July 2019.
13. Quantitative prediction of erythrocyte sickling for anti-polymerization activities in sickle cell disease. 60^th Annual Red Cell Meeting, New Haven, CT, Oct. 2018.
14. OpenRBC: A fast simulator of red blood cells at protein resolution. SIAM Annual Meeting, Pittsburgh, PA, July 2017.
15. Probing the twisted structure of sickle hemoglobin fibers via particle simulations. 20^th Biennial Hemoglobin Switching Conference, Pacific Grove, CA, Sept. 2016.
16. Shock tube ignition delay time study of RP-1/oxygen/argon mixtures. Stanford Undergraduate Visiting Research Symposium, Stanford, CA, Aug. 2012.
17. The feasibility analysis of small-sized commercial ice-storage air-conditioning system. 10^th National Symposium on Refrigerators, Air Conditioners and Compressors, Qingdao, Shandong, Aug. 2011.