Yiren Shen

Yiren Shen

he/him/his

Postdoctoral Researcher at University of Washington NSF AI institute in Dynamical Systems

Talks and presentations

Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design Using GNNs

July 09, 2026

Invited talk, NASA Advanced Modeling and Simulation (AMS) Seminars, Moffett Field, CA, USA

Conceptual aircraft design requires major engineering decisions under limited information. In practice, low-fidelity aerodynamic tools are routinely used because higher-fidelity simulations are often unavailable or too expensive for early-stage design. This creates a fidelity-cost gap: the level of analysis fidelity needed for confident decision-making is not available at the stage with the greatest downstream impact. This challenge is especially important in supersonic transport design, where nonlinear aerodynamic effects make considerations such as low-speed stability difficult to assess using conventional conceptual design tools.

Multi-Fidelity Aerodynamic Surrogate Modeling for Conceptual Design

May 28, 2026

Invited talk, SRI International Western ML Seminar, Menlo Park, CA, USA

This talk presents MF-VortexNet, a multi-fidelity aerodynamic surrogate framework that uses graph neural networks to learn field-level corrections to low-fidelity pressure predictions from high-fidelity CFD data. By operating on pressure-field information, MF-VortexNet improves interpretability, enables the evaluation of arbitrary aerodynamic quantities, and supports generalization across a prescribed design space. The talk has strong focus on introducing an open-source multi-fidelity aerodynamic dataset, empirical scaling behavior of surrogate error with training data size, and integration of the framework into a conceptual design environment for stability-constrained shape optimization.

VortexNet: A Graph Neural Network-Based Multi-Fidelity Surrogate Model for Field Predictions

August 01, 2025

Invited Talk, Altair Inc., Online

This talk introduces VortexNet, a graph neural network (GNN)-based surrogate model designed to bridge the fidelity gap between low-fidelity (LF) and high-fidelity (HF) aerodynamic predictions. VortexNet learns corrections to vortex lattice method (VLM) panel-wise loading coefficient fields using data from high-fidelity CFD simulations, enabling pressure coefficient field predictions across a range of delta wing geometries and free-stream conditions. The talk covers the model architecture, its prediction accuracy and generalizability across configurations, and its potential role in conceptual design and multidisciplinary design optimization (MDO).

AI-driven Simulations for Automotive Industry

January 06, 2025

Invited Talk, Li Auto Strategic Investment Meetings, Online

This talk surveys how AI-driven simulation, including machine learning-based surrogate models and multi-fidelity analysis, can accelerate engineering design and assessment workflows in the automotive industry.