Accelerate your simulation workflows with our deep learning platform

Alex is a Caltech's trained engineer and business developer with expertise in physics-based simulations. He launched his career by designing and building simulation models for diverse industries.

His early professional experience includes roles as a Simulation and Product Development Engineer at Saipem, one of the largest multinational oilfield services companies, and at Optotune, a Zurich-based startup specializing in manufacturing optical devices for the machine vision industry.

To complement his engineering background, he expanded his expertise into business strategy and management consulting within the financial services sector at UBS, where he led multi-million dollar projects.

During his academic tenure, he contributed to developing a transmission component for the automated measurement and quality control of Porsche Electric Axles.

He holds a Master’s degree in Applied Mechanics from the California Institute of Technology, where he specialized in Computational Mechanics. As a Researcher at Caltech, he developed virtual-experiment frameworks that significantly reduced R&D costs and timelines by integrating Finite Element simulations with optical algorithms. This project resulted in several publications in peer-reviewed journals, including Experimental Mechanics.

Jan-Hendrik is a distinguished researcher specializing in deep learning frameworks for engineering applications. His doctoral research at ETH Zurich and Columbia University introduced cutting-edge machine learning methods that advanced the design and analysis of engineering materials, with publications in top journals like PNAS and Nature Machine Intelligence.

One of his most notable projects demonstrated that generative AI can design metamaterials with complex mechanical properties, impacting fields from aerospace to biomedical engineering. He has also developed expertise in physics-based modeling, including physics-informed neural networks and integrating physical equations into generative frameworks.

He earned a Master’s degree in Mechanical Engineering from ETH Zurich, where he was awarded the prestigious ETH Medal for his thesis on viscoelastic truss metamaterials. He also holds a Bachelor's degree in Industrial Engineering from Technische Universität Braunschweig, supported by a German National Academic Foundation scholarship. His diverse research spans projects from analyzing regional economic inequalities using satellite data to studying diamond wire sawing for silicon wafer manufacturing.

Beyond academia, he has applied his skills in consulting and engineering. At Boston Consulting Group, he provided strategic advice for the MedTech and insurance sectors, and he gained engineering experience at Volkswagen AG in Wolfsburg, Germany, and Progress-Werk Oberkirch AG in Suzhou, China.

Prof. Kochmann is a leading expert in computational mechanics, solid mechanics, and machine learning-enhanced simulation techniques. His research combines theoretical, computational, and experimental approaches to optimize the mechanical behavior of materials.

He joined the faculty of the California Institute of Technology's Aerospace Department in 2011, where he advanced the field of computational mechanics and became a Full Professor of Aerospace. In 2017, he moved to ETH Zurich, where he currently serves as Professor of Mechanics and Materials in the Department of Mechanical and Process Engineering. He has also held leadership roles as Head of the Institute of Mechanical Systems and Deputy Head of the Department.

His research focuses on integrating machine learning with finite element methods (FEM) to accelerate simulation-driven engineering design. His work on deep learning for inverse material design, truss structures, and spinodoid metamaterials has led to breakthroughs in the discovery of architected materials with tailored properties. Additionally, his contributions to physics-informed neural networks (PINNs) have advanced the use of data-driven models for solving complex physical systems governed by partial differential equations.

His achievements have been recognized with prestigious awards, including the NSF CAREER Award, the Richard von Mises Prize, and an ERC Consolidator Grant. He also serves on the editorial boards of Computational Mechanics, International Journal of Solids and Structures, and Applied Mechanics Reviews.