Name: School of CSE Ph.D. Student Yanjie Tong

Date: Wednesday, March 4, 2026, at 12:00 p.m.

Location: Coda, Room 230 (Google Maps link)

Lunch provided!

Title: From Sparse Sensors to Continuous Fields: STRIDE for Spatiotemporal Reconstruction

Abstract: We introduce STRIDE (Spatio-Temporal Recurrent Implicit DEcoder), a novel deep learning framework for reconstructing high-dimensional spatiotemporal fields from sparse point-sensor measurements. Existing approaches often struggle to generalize across trajectories and parameter settings, or rely on discretization-tied decoders that do not naturally transfer across meshes and resolutions. Our proposed approach is a two-stage framework which maps a short window of sensor measurements to a latent state with a temporal encoder and reconstructs the field at arbitrary query locations with a modulated implicit neural representation (INR) decoder. Using the Fourier Multi-Component and Multi-Layer Neural Network (FMMNN) as the INR backbone improves representation of complex spatial fields and yields more stable optimization than sine-based INRs. We provide a conditional theoretical justification: under stable delay observability of point measurements on a low-dimensional parametric invariant set, the reconstruction operator factors through a finite-dimensional embedding, making STRIDE-type architectures natural approximators. Experiments on four challenging benchmarks spanning chaotic dynamics and wave propagation show that STRIDE outperforms strong baselines under extremely sparse sensing, supports super-resolution, and remains robust to noise.

Bio: Yanjie Tong is a second-year CSE Ph.D. student advised by Dr. Peng Chen. He earned both his B.S. in Mathematics and Physics and his B.Eng. in Energy and Power Engineering at Tsinghua University. His research focuses on methods for learning latent dynamics in low-dimensional spaces and their application to real-world problems.

About HotCSE

HotCSE is an academic seminar series to bring Ph.D. students in Computational Science and Engineering together to discuss interesting topics. The topics consist of high-performance computing, machine learning, data analysis, simulation, computational sustainability, medical informatics, etc.

The talks have always been enjoyable and have ranged from quite informal to formal conference style talks. Either chalks or slides can be used to help people understand your talk. It is also a great forum to practice conference talks and bounce around new ideas.

Currently the talks are sponsored by the School of Computational Science and Engineering. The goal of CSE is slightly broader than that of these talks - we want to bring more people outside CSE to discuss their related work here.