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In this episode, I sit down once again with Professor Oliver Niggemann to unravel the world of numerical machine learning—where traditional engineering meets cutting-edge AI. We explore real-world projects from diagnosing the International Space Station to designing safer bridges, smarter batteries, and even optimizing biodiversity. Oliver breaks down how fusing symbolic knowledge and neural networks is revolutionizing simulation, design, and problem-solving across industries.
If you’ve ever wondered how AI can speed up material discovery or what the future holds for interdisciplinary engineers, this conversation is for you. We dive into the power of surrogate models, the evolution of engineering education, and why tomorrow’s innovations demand both deep technical expertise and creative collaboration. Join us for a look at the next frontier in industrial AI.
https://new.siemens.com/global/en/company/topic-areas/artificial-intelligence.html
https://www.hsu-hh.de/ims/team/niggemann/
https://www.hsu-hh.de/
https://www.dlr.de/rd/en/desktopdefault.aspx/tabid-2441/3587_read-5637/
https://www.airbus.com/en/products-services/space
https://www.esa.int/ScienceExploration/HumanandRoboticExploration/Columbus
https://www.iosb.fraunhofer.de/
https://en.wikipedia.org/wiki/Physics-informedneuralnetworks
https://en.wikipedia.org/wiki/Surrogate_model
https://www.cusp.ai/
https://en.wikipedia.org/wiki/Geoffrey_Hinton
https://en.wikipedia.org/wiki/Yann_LeCun
https://www.deepmind.com/research/highlighted-research/alphafold
By Robert Weber / Peter Seeberg5
44 ratings
In this episode, I sit down once again with Professor Oliver Niggemann to unravel the world of numerical machine learning—where traditional engineering meets cutting-edge AI. We explore real-world projects from diagnosing the International Space Station to designing safer bridges, smarter batteries, and even optimizing biodiversity. Oliver breaks down how fusing symbolic knowledge and neural networks is revolutionizing simulation, design, and problem-solving across industries.
If you’ve ever wondered how AI can speed up material discovery or what the future holds for interdisciplinary engineers, this conversation is for you. We dive into the power of surrogate models, the evolution of engineering education, and why tomorrow’s innovations demand both deep technical expertise and creative collaboration. Join us for a look at the next frontier in industrial AI.
https://new.siemens.com/global/en/company/topic-areas/artificial-intelligence.html
https://www.hsu-hh.de/ims/team/niggemann/
https://www.hsu-hh.de/
https://www.dlr.de/rd/en/desktopdefault.aspx/tabid-2441/3587_read-5637/
https://www.airbus.com/en/products-services/space
https://www.esa.int/ScienceExploration/HumanandRoboticExploration/Columbus
https://www.iosb.fraunhofer.de/
https://en.wikipedia.org/wiki/Physics-informedneuralnetworks
https://en.wikipedia.org/wiki/Surrogate_model
https://www.cusp.ai/
https://en.wikipedia.org/wiki/Geoffrey_Hinton
https://en.wikipedia.org/wiki/Yann_LeCun
https://www.deepmind.com/research/highlighted-research/alphafold

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