Physics-informed AI-enhanced Engineering Design and Simulation Ecosystem

Lecture / Panel
For NYU Community



Soumalya Sarkar, PhD

Sr. Principal Scientist, AI Discipline,

Raytheon Technologies Research Center (RTRC)

Dr. Soumalya Sarkar, Sr. Principal Scientist of the AI Discipline in Raytheon Technologies Research Center (RTRC), has 7 years of experience in Scientific Machine Learning, Physics-informed AI, Deep Learning, NLP and Knowledge graph, Black-box optimization, Multi-modal Sensor Fusion. He serves as the PI/co-PI and ML task lead for government (ARPA-E DIFFERENTIATE, DARPA, and DOE) and corporate-funded research programs in the areas of physics-informed AI, AI-guided engineering simulation, design, materials discovery. He has co-authored 55 peer-reviewed publications including 20 Journal papers, 2 book chapters and 10 US patent applications and 5 awarded patents. Dr. Sarkar has received numerous awards including the 2021 “Technical Excellence Award (TEA)" (highest individual technical award at RTRC), two annual “Outstanding Achievement Award (OAA)" from RTRC in 2018 and 2022, and invitation as one of the 100 early-career engineers/faculties from US industry, universities, and national labs to attend National Academy of Engineering’s (NAE) 2020 US Frontiers of Engineering symposium. He has received PhD on data-driven learning of complex systems and double masters in Math and ME from the Pennsylvania State University.


Aerospace and defense industries are actively developing Artificial Intelligence (AI) solutions to fundamentally transform their design, simulation, and development processes to achieve better productivity, energy savings and safety standards. AI is making impacts in industries involving speech and natural language understanding, e-commerce, social networks, gaming, medicine, and robotics, but it has yet to demonstrate its full potential in revolutionizing the development of core engineering and manufacturing industries. The key challenges include long design cycles, siloed and manual design across scales, lack of learning from noisy heterogeneous knowledge sources, and large design and processing space of safety-critical systems. Recently, researchers have been applying off-the-shelf AI/ML tools to overcome some of these challenges via surrogate-based design optimization, multi-fidelity learning and physics-constrained emulators. Still, large-scale engineering and simulation design problems with AI become prohibitively expensive and non-deployable due to challenges such as, (i) lack of interpretability and trust on black-box ML-surrogate models, (ii) high computational overhead for high-fidelity solvers (ii) enterprise-level heterogeneity of model/software, (iii) non-differentiability and lack of model visibility and compatibility, (iv) data scarcity, (v) multiple data sources with different uncertainties. This talk will discuss a novel multi-X AI (X = source, scale and/or fidelity) ecosystem developed by RTRC, which is built upon the concepts of physics-informed and interpretable ML, multi-fidelity leaning, data-efficient surrogate modeling, Cost-aware blackbox optimization, and non-intrusive physics incorporation into learning. The ecosystem has demonstrated 2-10X speed-up and better solutions in diverse domains including rapid materials discovery, gas turbine engine design, digital twin construction, simulation calibration, optimal scenario generation, micro/power electronics systems design, manufacturing process optimization and in-situ monitoring.