Abstract

Physics simulation increasingly sits in the loop of interactive content creation, digital twins, and embodied AI. A central dilemma cuts across these applications: the tradeoff between simulation speed and fidelity, especially at scale. In practice, an equally limiting barrier is interactive control. Artists and designers need coarse previews they can manipulate in real time, with confidence that refinement will not overturn their decisions. Standard multiresolution approaches often fall short because coarse solutions can drift, and refinement can introduce surprises due to numerical stiffening, geometric inconsistency, and contact.

To address this challenge, my research program introduces Progressive Simulation, a general-purpose, coarse-to-fine level-of-detail (LOD) framework for physics simulation that brings and generalizes ideas from progressive rendering and progressive geometry to the simulation setting. The core idea is to produce a fast, coarse, artifact-free, predictive preview under tight computational budgets for rapid iteration, then apply consistent refinement across resolutions that converges to a richly detailed, high-fidelity result as more resources or stricter accuracy requirements become available. In this talk, I will present methods that realize this approach in both progressive quasistatics and progressive dynamics, with results spanning cloth, shells, and volumetric objects, enabling experimental design, interactive staging, and physics-based storytelling. Finally, I will outline future directions toward broader applicability, including multi-fidelity, multi-physics simulation systems powered by scalable solvers.

Bio

Jiayi (Eris) Zhang is a fifth-year PhD candidate in Computer Science at Stanford University, advised by Prof. Doug James, and works closely with Dr. Danny Kaufman at Adobe. She received her BS in Computer Science and Mathematics from the University of Toronto, where she conducted research with Prof. Alec Jacobson. Her research spans physics simulation, geometry processing, numerical optimization, and spatial intelligence, with a focus on building algorithms, models, and tools that enhance human productivity and creativity in science, engineering, and interactive authoring workflows. She is a Stanford Reed-Hodgson Fellow, a WiGRAPH Rising Star, and an MIT EECS Rising Star, and has received the Adobe Women-in-Technology Scholarship, the Roblox Graduate Fellowship, and the NVIDIA Graduate Fellowship. She has also interned at Adobe and NVIDIA across multiple summers.