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Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

Ian Smith, Bernd Domer, Etienne Fest
2003
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Ian Smith, Bernd Domer, Etienne Fest
2003
Journal Articles

Official source

Related publications (11)

https://infoscience.epfl.ch/entities/publication/ca5f13a9-aba3-42e8-ac66-d544b9efaff3

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Related publications (11)

An integrative computational modeling approach for Drosophila motor control

Pembe Gizem Ozdil

Animal motor control arises from the intricate interplay between neural circuits, biomechanics, and sensory feedback. Deciphering how these components interact is essential for uncovering the fundamental principles of movement generation. This dissertation ...

EPFL2025

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Devavrat Tomar

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

EPFL2025

DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Jürg Alexander Schiffmann, Soheyl Massoudi, Timothy Horvath, Joseph Bejjani, Dogukan Üstün

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural ...

ASME International2025