# Saakaar Bhatnagar

> Physical AI @ Luminary | Stanford University

Location: Sunnyvale, California, United States
Profile: https://flows.cv/saakaar

Saakaar Bhatnagar is a seasoned engineer and researcher with over 7 years of experience in physics-informed machine learning and artificial intelligence. At Luminary Cloud, he works on the go-to-market team, leveraging his expertise in large scale physics simulation, data processing and machine learning.  Working closely with customers, his work enables engineering teams to fully realize the improvements AI tools can offer in complex product design.

Previously at Altair Engineering, he led technical initiatives to incorporate machine learning into Altair's flagship physics simulation softwares, focusing on accelerating performance, improving user experience, and expanding the software's applications to new industry sectors. 

Saakaar holds a Master’s degree in Aerospace Engineering from Stanford University and earned the Gold Medal in Aerospace Engineering from IIT Kanpur. His research has been widely published in leading peer-reviewed journals and conferences, contributing valuable insights to the engineering applications of Artificial Intelligence.

He is passionate about bridging the gap between AI and physics, consistently pushing the boundaries of innovation to solve real-world engineering problems.

## Work Experience
### Forward Deployed Engineer @ Luminary
Jan 2025 – Present | San Mateo, CA
Building and deploying Physical AI

### Solver Development Engineer (ML) @ Altair
Jan 2021 – Jan 2025 | San Francisco Bay Area
Led the integration of advanced data science, machine learning, and artificial intelligence methodologies into Altair's flagship physics simulation software, AcuSolve. This strategic initiative successfully broadened the software’s applicability across diverse industry sectors while significantly enhancing user experience and simulation capabilities. Notable achievements include:

- Developed innovative, efficient, and high-performance algorithms for parameter fitting in physics-based battery models. These advancements enabled precise simulation of complex battery phenomena, including health degradation and thermal runaway, leading to published scientific and technical articles.

- Collaborated extensively with industry end-users to integrate newly developed tools and algorithms into their operational workflows, ensuring practical applicability and delivering tangible improvements through cutting-edge technology.

### Short Term Research Scholar @ University of Michigan
Jan 2018 – Jan 2019 | Ann Arbor, Michigan, United States
- Project was launched with the goal of using ML-based flow simulators to reduce design time/iterations in automotive aerodynamics.
- Created and Implemented an Encoder-Decoder architecture to predict airfoil flow fields using Convolutional Neural Networks , by inputing just the airfoil image and flow conditions
-Trained the network to obtain a tool which can generalize well to unseen examples and predict test case flow fields with reasonable accuracy
- Used a modified loss function based on gradient loss minimization to improve flow field predictive accuracy.
- Published results as joint first author.


## Education
### Master's degree in Aerospace, Aeronautical and Astronautical Engineering
Stanford University

### Bachelor's degree in Aerospace, Aeronautical and Astronautical Engineering
Indian Institute of Technology, Kanpur


## Contact & Social
- LinkedIn: https://linkedin.com/in/saakaar-bhatnagar-b7132a136

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Source: https://flows.cv/saakaar
JSON Resume: https://flows.cv/saakaar/resume.json
Last updated: 2026-04-10