Computational Intelligence and Data Science framework and the KadiAI interface (CIDS and KadiAI)
Keywords: machine learning, artificial intelligence, neural networks, active learning, mechanics, materials science
CIDS is a framework for Artificial Intelligence (AI) and Machine Learning (ML) for engineering, materials, and natural sciences applications. It combines models, functions, and pipelines from libraries such as tensorflow/keras, sklearn, scipy, and pandas to build modular, flexible, and reproducible AI models.
The interface KadiAI integrates AI tools, such as CIDS, seamlessly into Kadi workflows and interacts with Kadi's repositories and data management features.
Publications
Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models
Koeppe A, Bamer F, Selzer M, Nestler B, Markert B - Frontiers in Materials - 2022
High-fidelity simulations and data-driven insights on rate-governing phases in duplex and triplex systems during isotropic normal grain growth
Amos P, Koeppe A, Perumal R, Nestler B - Physical Review Materials - 2022
Mechanics 4.0
Koeppe A, Hesser D, Mundt M, Bamer F, Selzer M, Markert B - Handbook Industry 4.0 - 2022
Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate
Zhao Y, Schiffmann N, Koeppe A, Brandt N, Bucharsky E, Schell K, Selzer M, Nestler B - Frontiers in Materials - 2022
An artificial intelligence approach to model nonlinear continua by intelligent meta‐elements
Koeppe A, Bamer F, Markert B - PAMM - 2021
Workflow concepts to model nonlinear mechanics with computational intelligence
Koeppe A, Bamer F, Selzer M, Nestler B, Markert B - PAMM - 2021
Tiefes Lernen in der Finite-Elemente-Methode
Koeppe A - Dissertation - 2021
Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network
Mundt M, Koeppe A, David S, Witter T, Bamer F, Potthast W, Markert B - Frontiers in Bioengineering and Biotechnology - 2020
An efficient Monte Carlo strategy for elasto-plastic structures based on recurrent neural networks
Koeppe A, Bamer F, Markert B - Acta Mechanica - 2019
Efficient numerical modeling of 3D-printed lattice-cell structures using neural networks
Koeppe A, Hernandez Padilla C, Voshage M, Schleifenbaum J, Markert B - Manufacturing Letters - 2018
