Atharva Hans, Ph.D.
Lead AI Scientist
At Agentic Engineering Systems, Dr. Atharva Hans spearheads the development of autonomous AI workflows. By combining physics-informed deep learning with advanced agentic architectures, he transforms complex, multi-step engineering and computational processes into streamlined, intelligent solutions.
- Agentic AI Systems
- Physics-Informed Deep Learning
- Scientific Machine Learning
Architecting Autonomous Workflows
Dr. Hans is the driving force behind the design and implementation of agentic AI solutions at Agentic Engineering Systems. His primary focus is on building intelligent, autonomous agents that seamlessly integrate machine learning, physical simulations, and large-scale data analysis. These systems go beyond simple data processing—they autonomously execute and manage complex scientific workflows, drastically accelerating research, discovery, and product development for enterprise clients.
Physics-Informed Deep Learning
Standard machine learning often struggles with complex physical laws, but Dr. Hans specializes in AI that understands the physical world. His deep expertise in scientific machine learning and Physics-Informed Neural Networks (PINNs) allows him to develop data-driven models that respect the fundamental laws of physics. This approach extracts precise, quantitative insights from highly complex systems, bridging the gap between raw data and reliable engineering models.
A Foundation in Advanced Computational Science
Dr. Hans’s applied industry expertise is built upon rigorous academic research. Holding a Ph.D. in Mechanical Engineering from Purdue University, he serves as a Research Scientist at the Predictive Science Laboratory (PSL) and the Regenstrief Center for Healthcare Engineering. His foundational work in computational methods and medical image analysis—particularly in 4D flow MRI and cardiovascular hemodynamics—demonstrates his unique ability to solve some of the most intricate, data-heavy challenges in both engineering and biomedicine.