Symposium

M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain

Belongs to:
TopicM: Modelling and Simulation

The focus of this symposium lies on data-driven methods that enable the design of new materials for desired properties, and on optimal control and planning methods for the corresponding processes. Such methods are typically based on the inversion of the process-structure-properties chain. Recent work shows that mappings between the individual elements of this chain can efficiently be established via data-driven methods that integrate domain knowledge, such as physical relations. Challenges in the modeling process are for example finding appropriate microstructure representations, accurate and robust machine learning approaches, and methods to efficiently generate data in the space of interest. The symposium provides a platform to discuss latest approaches tackling these challenges. It also aims at bringing together the communities of materials science, of data-driven modeling and machine learning, specifically the communities working on data-driven materials design and process design.


Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
Data-efficient, simulation-based sample generation for the modelling of process-structure-property relations using machine learning

Lukas Morand Johannes Dornheim Tarek Iraki Prof. Dr. Norbert Link Dr. Dirk Helm

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
WEB Data-driven prediction of structure-property relationships using bonded-particle model and artificial neural networks

Prof. Maksym Dosta Vasyl Skorych Tsz Tung Chan

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
A Digitalized Machine Learning-Assisted Workflow to Study Microstructural Fatigue Damage Evolution and Crack Initiation

Ali Riza Durmaz Dr. Thomas Straub Prof. Dr. Chris Eberl

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
Efficient data structures for model-free data-driven computing

Robert Eggersmann Prof. Dr. Laurent Stainier Prof. Dr. Michael Ortiz Prof. Dr. Stefanie Reese

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
Enabling intelligent Mg-sheet processing utilizing efficient ML-algorithm

Mohamadreza Shariati Dr. Daniel Höche Dr. Jan Bohlen Dr. Gerrit Kurz Dr. Dietmar Letzig Prof. Dr. Wolfgang Weber

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
Importance of performance assessment for machine learning models of entropy of all solid substances

Dr. Florian Tang Dr. Moritz to Baben

Lecture M04: Data-driven Modeling and Optimization of the Materials Properties-Structure-Process Chain
Simulation of Spinning Processes with Experimental Validation

Stefan Hermanns Dr. Walter Arne