Peter Yichen Chen

🚀 Excited to kick off the Graphics × Science Workshop at #SIGGRAPH2026!

Computer graphics is becoming a foundational tool for scientific discovery. From computational imaging and molecular modeling to physical simulation, robotics, manufacturing, and AI, graphics is helping us model, understand, and design the physical world.

Looking forward to an exciting program featuring 3 keynote speakers and 58 highlighted papers.

🔗 graphics4science.github.io/2026/

#AI4Science #ComputerGraphics #ScientificComputing #Simulation #NVIDIA #NVIDIAOmniverse #NVIDIAAI

Grateful to all my mentors, mentees, and collaborators for being part of this journey!

Excited to continue pushing the boundaries of simulation for Physical AI.

#CVPR2026 Zero simulation data. Zero annotations. One physics foundation model that generalizes across arbitrary geometries.

Introducing PhysSkin: a transformer-based, physics-informed self-supervised model that learns real-time simulation straight from static 3D shapes. The representation is discretization-agnostic — a single model generalizes across object categories, topologies, and resolutions, and works directly on 3D Gaussians.

Under the hood: a transformer point-cloud encoder extracts latent shape features, while a cross-attention decoder aggregates both surface and volumetric information.

Sun, Jun 7, 2026 · 10:45 AM–12:45 PM PDT · ExHall F 348
zju3dv.github.io/PhysSkin/

What happens when differentiable simulation meets diffusion models? You get a foundation model that is both 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 AND 𝗴𝘂𝗮𝗿𝗮𝗻𝘁𝗲𝗲𝗱 𝘁𝗼 𝗼𝗯𝗲𝘆 𝗽𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀.

📢 Excited to share our latest work accepted to #ICLR2026: "Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection."

🧬 Foundation models like AlphaFold3 and Boltz have transformed biomolecular structure prediction — yet they still hallucinate physically invalid structures: steric clashes, distorted covalent geometry, broken stereochemistry.

The root cause? Current predictors are trained to match empirical distributions — they never enforce physical validity as a 𝗵𝗮𝗿𝗱 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁.

The secret sauce? A 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗯𝗹𝗲 𝗚𝗮𝘂𝘀𝘀-𝗦𝗲𝗶𝗱𝗲𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝘂𝗹𝗲 that takes provisional atom coordinates from the diffusion model and projects them onto the nearest physically valid configuration. By exploiting the locality and sparsity of atomic constraints, it converges stably and fast at scale. The module plugs into existing frameworks end-to-end via implicit differentiation — treating physical validity as a first-class citizen during 𝗯𝗼𝘁𝗵 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲.

With our projection module in place, just 𝟮 𝗱𝗲𝗻𝗼𝗶𝘀𝗶𝗻𝗴 𝘀𝘁𝗲𝗽𝘀 suffice to match the structural accuracy of 200-step diffusion baselines — delivering a ~𝟭𝟬× 𝘄𝗮𝗹𝗹-𝗰𝗹𝗼𝗰𝗸 𝘀𝗽𝗲𝗲𝗱𝘂𝗽 while guaranteeing physical validity. Across six benchmarks (CASP15, PoseBusters, AF3-AB, dsDNA, RNA-Protein, and more), our model closes the gap between guaranteed physical validity and state-of-the-art structural accuracy.

Joint work across UBC, MIT, NVIDIA, PKU, U of Utah, and Foundry Biosciences.

📄 Project: chensiyuan030105.github.io/ProteinGS/
💻 Code: github.com/chensiyuan0301…

#SIGGRAPHAsia2025 The future of embodied intelligence will blend soft-ish and rigid-ish components. Simulating such systems has traditionally been slow and often considered impractical. We’re excited to share our latest effort to accelerate these simulations using AI-enhanced physics engines. The secret sauce? Precise handling of neural field gradient discontinuities.

Come check out Mengfei and ChangYue’s presentation at SIGGRAPH Asia 2025!
mengfeill.github.io
changy1506.github.io