Prof. Xiang Yang

Dr. Xiang Yang is the Kenneth K. & Olivia J. Kuo Early Career Professor and an Associate Professor of Mechanical Engineering at Penn State. He received his Ph.D. in Mechanical Engineering from Johns Hopkins University in 2016 and was a postdoctoral fellow at Stanford University's Center for Turbulence Research before joining Penn State in 2018. His research combines high-fidelity simulation, physics-based theory, data-driven modeling, and AI-enabled scientific workflows to study turbulent flows and develop predictive models for aerospace, energy, and environmental applications. His recent work explores how large language model agents, connected to scientific software and validation tools, can accelerate the path from formulation to computational evidence and enduring scientific insight. He received the APS Andreas Acrivos Dissertation Award in Fluid Dynamics and an AFOSR Young Investigator Program award. He has authored or co-authored more than 100 journal articles.

Predictive computational fluid dynamics depends on turbulence models that are affordable enough for design yet accurate across the wide range of flows encountered in practice. Machine learning can add descriptive power, but many data-driven corrections perform well only near their training conditions and can degrade a trusted baseline when extrapolated. This seminar asks how machine learning can improve turbulence models without discarding the physical structure that makes them robust.

I will present an extrapolative, progressive framework in which missing physics are learned one deficiency at a time while algebraic and asymptotic constraints preserve essential baseline calibrations, including the law of the wall. Examples span jet spreading, separated-flow recovery, stall, secondary flows, pressure-gradient boundary layers, and compressible wall layers. Results across changes in geometry and Reynolds number show that flexibility alone is not enough: reliable generalization requires physical constraints, interpretable diagnostics, and verification and validation outside the training regime.

The seminar will then look beyond model correction to emerging roles for large language model agents in turbulence research. Prototype case studies include the development of transparent wall models, an AI-guided search for a density-only transformation for compressible wall turbulence, and reusable automation for end-to-end CFD workflows. The goal is not to replace fluid mechanics, but to combine scientific judgment with AI's breadth, speed, and computational persistence—turning data and simulation into models and physical insight that remain transparent, reproducible, and accountable.

Seminar date and time: December 1, 11 AM ET.

Zoom information here.

Prof. Ameya Jagtap

I am an Assistant Professor (tenure-track) in the Department of Aerospace Engineering at Worcester Polytechnic Institute (WPI), USA. Prior to joining WPI, I served as an Assistant Professor of Applied Mathematics (Research) at Brown University for three and a half years. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics.

My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. 

TBD

Seminar date and time: TBD.

Zoom information here.

You?

Please reach out to rmaulik@purdue.edu if you are interested in presenting in the ISCL Seminar Series! Graduate students and postdocs are particularly encouraged to present their work.

Seminar date and time: TBA.

Zoom link here.