Ersatzmodelle

Analytische Methoden und Analyse von Werkstoffen

Mathematische Modelle in der Werkstoff- und Prozessbeschreibung

Modelle in der Werkstoff- und Prozessbeschreibung

Ersatzmodelle

Ersatzmodelle sind recheneffiziente Approximationen komplexer physikalischer Modelle oder Experimente. In den Materialwissenschaften nähern sie z.B. Finite‑Elemente‑Simulationen, Phasendiagrammrechnungen oder Prozessmodelle an, basierend auf Stichproben im Eingaberaum. Verwendet werden Polynome, Gaußprozesse, neuronale Netze oder reduzierte Ordnungsmodelle. Sie ermöglichen schnelle Sensitivitätsanalysen, Optimierung, Unsicherheitsquantifizierung und echtzeitfähige Prozessregelung.

Verwandte Tags

Maschinenlernen

Künstliche Intelligenz

Bayes-Optimierung

Datengetriebene Modelle

mehrskalige Modellierung

Mehrobjektoptimierung

Materials Modelling, Simulation and Data

inverse Gestaltung

Datenmanagement

3D Materials Data

Werkstoffentdeckung

Prozessüberwachung

Datengetriebene Materialgestaltung

Unsicherheitsquantifizierung

Digitalisierung & Künstliche Intelligenz

Datenanreicherung

Aufbau- und Verbindungstechnik

Forschung und Entwicklung

Werkstoffe der Energietechnik

datengetriebenes Modell

hybride Modellierung

Materialcharakterisierung & -prüfung

Finite-Element-Simulationen

datengetriebene Methoden

Materialmodelle

Festkörperbatterien

In situ 2D und 3D Charakterisierung

prädiktive Modelle

Gaußsche Prozessregression

Anomalieerkennung

Werkstoffverhalten unter mechanischer Beanspruchung

Quantitative Gefügeanalyse

Automobil und Fahrzeuge

Neue Inhalte

Accelerating Materials Discovery: Hybrid Generative AI and Physics-Informed Inverse Design of Magnesium Alloys

Guru, M. (Speaker)¹

Development of Model and Sensor-Supported Processes for Massive Forming of Titanium Materials With Optimal Microstructure

Farooqui, M.M.U. (Speaker)¹; Bisterfeld, F.¹; Katrakova-Krüger, D.¹; Witulski, T.²; Wolf, C.¹

Microstructural Data Science: korrelative Mikroskopie und KI als Werkzeuge für datengetriebenes Werkstoffdesign

Stiefel, M. (V)¹; Müller, M.²; Bachmann, B.-I.²; Britz, D.²; Staudt, T.³; Weikert-Müller, M.³; Mücklich, F.²

59. Metallographie-Tagung 2025 - Materialographie

Accelerating Alloy Discovery through Autonomous Multi-Objective Optimisation and Integrated ICME Frameworks

Mamun, O.¹; Saikia, U.¹; Mukherjee, A.¹; Tappe, A.¹; Ebna Hai, B.S.M. (Speaker)¹

NextGen Materials 2025: The Convergence of Living Essence and Engineered Innovation

A Computational Multiscale Framework for the Design of Programmable Metamaterials

Leichner, A. (Speaker)¹; Andrä, H.²; Palmer, T.²; Hemrbock, H.²; Besler, S.³; Wenz, F.⁴

NextGen Materials 2025: The Convergence of Living Essence and Engineered Innovation

Design of Analog Computers: Building Blocks and Advanced Methods for Tiny Analog Machine Learning with Surrogate Modeling

Bosse, S. (V)¹

International Conference on System-Integrated Intelligence - SysInt 2025

Holistic Evaluation of Materials in the early Product Development

Jilg, A. (Speaker)¹; Huberth, F.¹

MaterialsWeek 2025

Highlight Lecture

Materials Design – A Modern Approach

Großmann, W.¹; Eilermann, S.¹; Rensmeyer, T.¹; Liebert, A.¹; Hohmann, M.¹; Wittke, C. (Speaker)¹; Niggemann, O.¹

Machine Learning-Guided Discovery of Enhanced High Entropy Alloys through Advances Ab-Initio Approaches

Moitzi, F. (Speaker)¹; Hodapp, M.¹; Peil, O.¹; Romaner, L.²; Ruban, A.¹

Surrogate Models for Woven Composites Homogenization with Deep Multi-Domain Adaptation and Contrastive Learning

Ghane, E. (Speaker)¹; Taghiyarrenani, Z.²

Beliebte Inhalte

Microstructural Data Science: korrelative Mikroskopie und KI als Werkzeuge für datengetriebenes Werkstoffdesign

Stiefel, M. (V)¹; Müller, M.²; Bachmann, B.-I.²; Britz, D.²; Staudt, T.³; Weikert-Müller, M.³; Mücklich, F.²

59. Metallographie-Tagung 2025 - Materialographie

Development of Model and Sensor-Supported Processes for Massive Forming of Titanium Materials With Optimal Microstructure

Farooqui, M.M.U. (Speaker)¹; Bisterfeld, F.¹; Katrakova-Krüger, D.¹; Witulski, T.²; Wolf, C.¹

Accelerating Alloy Discovery through Autonomous Multi-Objective Optimisation and Integrated ICME Frameworks

Mamun, O.¹; Saikia, U.¹; Mukherjee, A.¹; Tappe, A.¹; Ebna Hai, B.S.M. (Speaker)¹

NextGen Materials 2025: The Convergence of Living Essence and Engineered Innovation

A Computational Multiscale Framework for the Design of Programmable Metamaterials

Leichner, A. (Speaker)¹; Andrä, H.²; Palmer, T.²; Hemrbock, H.²; Besler, S.³; Wenz, F.⁴

NextGen Materials 2025: The Convergence of Living Essence and Engineered Innovation

Design of Analog Computers: Building Blocks and Advanced Methods for Tiny Analog Machine Learning with Surrogate Modeling

Bosse, S. (V)¹

International Conference on System-Integrated Intelligence - SysInt 2025

Inverse design of materials via Bayesian optimization

Li, Z. (Speaker)¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Accelerating Materials Discovery: Hybrid Generative AI and Physics-Informed Inverse Design of Magnesium Alloys

Guru, M. (Speaker)¹

Data-Driven Thermal and Percolation Analyses of 3D Composite Structures with Interface Resistance

Fathidoost, M. (Speaker)¹; Yang, y.¹; Oechsner, M.¹; Xu, B.-X.¹

FEMS EUROMAT 2023

Application of deep neural networks for computationally efficient thermal modelling of metal additive manufacturing

Gh Ghanbari, P. (Speaker)¹; Hosseini, E.¹; Mazza, E.²

Computational Discovery of Ultra-Strong, Stable, and Lightweight Refractory Multi-Principal Element Alloys

Elder, K. (Speaker)¹; Berry, J.¹; Perron, A.²; Bocklund, B.¹; Krajewski, A.³; Rietema, C.¹; Shittu, J.¹; Sims, Z.¹; Henderson, H.¹; Baker, A.¹; Voisin, T.¹; McCall, S.¹; McKeown, J.¹

FEMS EUROMAT 2023

Modeling Yield Surfaces of Nanoporous Metals in Multiaxial Stress Space using Machine Learning Approaches

Dyckhoff, L. (Speaker)¹; Huber, N.¹

Surrogate Models for Woven Composites Homogenization with Deep Multi-Domain Adaptation and Contrastive Learning

Ghane, E. (Speaker)¹; Taghiyarrenani, Z.²

Invited Lecture

Optimizing strain prediction and process design of forged parts by modeling and deploying a machine learning surrogate

Matsuo, M. (Speaker)¹; Lopez Santaella, M.¹; Ihne, J.¹; Hartmaier, A.²; Stricker, M.²

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Invited Lecture

Active learning for data-efficient optimisation of materials and processes

Todorović, M. (Speaker)¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Machine Learning-Guided Discovery of Enhanced High Entropy Alloys through Advances Ab-Initio Approaches

Moitzi, F. (Speaker)¹; Hodapp, M.¹; Peil, O.¹; Romaner, L.²; Ruban, A.¹

Highlight Lecture

Materials Design – A Modern Approach

Großmann, W.¹; Eilermann, S.¹; Rensmeyer, T.¹; Liebert, A.¹; Hohmann, M.¹; Wittke, C. (Speaker)¹; Niggemann, O.¹

Oral-Poster-Präsentation

Optimale Auslegung des RTM-Prozesses durch den Einsatz von Surrogate-Modellen

Droste, D. (V)¹; Dimassi, A.¹

Verbundwerkstoffe - 24. Symposium Verbundwerkstoffe und Werkstoffverbunde

A surrogate model for graphene-based conductor materials and towards the development of an ontology-based digital twin

Teichert, F. (Speaker)¹; Fuchs, F.¹; Schulze, P.²; Schuster, J.¹; Stoll, M.³

Holistic Evaluation of Materials in the early Product Development

Jilg, A. (Speaker)¹; Huberth, F.¹

MaterialsWeek 2025

The assessment of uncertainties in the degradation model of biodegradable magnesium-based implants

Albaraghtheh, T. (Speaker)¹; Willumeit-Römer, R.¹; Zeller-Plumhoff, B.¹

Data-driven emulators for microscale permeability prediction

Natarajan, D.K. (Speaker)¹; Schmidt, T.²; Cassola, S.²; Duhovic, M.²; May, D.²; Nuske, M.¹; Dengel, A.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

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