TP4b: Neural representations for dynamical physical systems
Tue, 27 Oct, 15:30 - 17:35 PT (UTC -7)
Location: Room 4
Session Type: Lecture
Session Co-Chairs: Hassan Mansour, Mitsubishi Electric Research Laboratory, United States and Joshua Rapp, Mitsubishi Electric Research Laboratory, United States
Track: Adaptive Systems, Machine Learning, and Data Analytics
Tue, 27 Oct, 15:30 - 15:55 PT (UTC -7)

TP4b.1: Towards a rate-distortion theory for implicit neural representations
Quang Nguyen, Sara Fridovich-Keil, Georgia Institute of Technology, United States
Tue, 27 Oct, 15:55 - 16:20 PT (UTC -7)

TP4b.2: Neural-Implicit Fields for Parameterized-PDE-Constrained Inference in Fluid Dynamic Systems
Samuel Grauer, Pennsylvania State University, United States; Ke Zhou, Huawei Technologies Co., Ltd., China; Rui Tang, Guanguan Ke, Pennsylvania State University, United States; Joseph Molnar, U.S. Naval Research Laboratory, United States
Tue, 27 Oct, 16:20 - 16:45 PT (UTC -7)

TP4b.3: ScoreField: Neural Inverse Scattering with Score-Based Generative Priors
Wenhan Guo, Yuan Gao, Yu Sun, Johns Hopkins University, United States
Tue, 27 Oct, 16:45 - 17:10 PT (UTC -7)

TP4b.4: Single View Camera-Based Dynamic Airflow Sensing with Physics Informed Neural Representations
Kevin Tandi, University of California San Diego, United States; Arjun Teh, Carnegie Melon University, United States; Wael Hajj Ali, Joshua Rapp, Hassan Mansour, Mitsubishi Electric Research Laboratories, United States
Tue, 27 Oct, 17:10 - 17:35 PT (UTC -7)

TP4b.5: Neural Fields for Revealing the Invisible Universe
Brandon Zhao, Caltech, United States; Aviad Levis, University of Toronto, Canada; Liam Connor, Harvard University, United States; Pratul Srinivasan, Google DeepMind, United States; Marianna Foschi, California Institute of Technology, United States; Antonio Fuentes, Jose Gomez, Instituto de Astrofisica de Andalucia, Spain; Diana Scognamiglio, Olivier Doré, NASA, United States; Katherine Bouman, California Institute of Technology, United States