Minisymposia

Plenary and Semi Plenary lectures will be complemented by Minisymposia organized by recognized experts in targeted research areas and related to all the important topics of the conference.

Participants interested in organizing a Minisymposium as part of DTE – AICOMAS 2025 Conference are invited to send an email to dte_aicomas@cimne.upc.edu.

The list of confirmed Minisymposia follows:

Machine Learning, Data-Driven Approaches, and Scientific Computing in Engineering

MS001 Scientific Deep Learning Approaches for realtime forecast and calibration of digital models

Organized by: T. Bui-Thanh (The University of Texas at Austin, United States)

Keywords: calbration, digital models, digital twins, forecast, scientific deep learning

MS002 Enabling Technologies for Scientific Machine Learning and Reduced Order Modeling

Organized by: A. Coutinho (Federal University of Rio de Janeiro, Brazil), A. Reali (University of Pavia, Italy) and G. Rozza (SISSA, Mathematics Area, Italy)

Keywords: digital twins, scientific deep learning, scientific machine learning, uncertainty quantification

MS003 Physics-Informed Machine Learning for Surrogate Models in Continuum Mechanics

Organized by: V. Trávníková (RWTH Aachen University, Germany) and M. Möller (Delft University of Technology, Netherlands)

Keywords: CFD, engineering design, neural operators, PINNs

MS004 Machine Learning Advances in Earth Sciences and Engineering

Organized by: J. E. Santos (Los Alamos National Laboratory, United States), T. Kadeethum (Sandia National Laboratories, United States), M. Fernández-Godino (Lawrence Livermore National Laboratory, United States), J. Bakarji (American University of Beirut, Lebanon) and D. O'Malley (Los Alamos National Laboratory, United States)

Keywords: architectures with hard constraints, digital twins, earth sciences, foundation models for physical sciences, GenAI for science, material science, multimodal data fusion, open-source scientific datasets/benchmarks

MS005 Data-Driven Methods and Digital Twin Applications in Geotechnical and Ground Engineering

Organized by: J. Ninic (University of Birmingham, United Kingdom), H. Bui (Helmholtz Center Hereon, Germany), B. Cao (Ruhr University Bochum, Germany), H. Liravi (University of Birmingham, United Kingdom) and G. Meschke (Ruhr University Bochum, Germany)

Keywords: digital models, digital twins, geotechnics, infrastructures, real-time monitoring, scientific machine learning

MS006 Uncertainty quantification and design optimization of complex structures and innovative construction process using machine learning

Organized by: D. Do (University of Orleans, France) and D. HOXHA (Uni, France)

Keywords: engineering design, geotechnics, industrial applications of AI, inverse problems, scientific machine learning, surrogate modeling, uncertainty quantification

MS007 Advances in ML-hybrid methods for computational solid mechanics problems

Organized by: P. Pantidis (New York University Abu Dhabi, United Arab Emirates), M. Mobasher (New York University Abu Dhabi, United Arab Emirates) and F. Aldakheel (Leibniz University, Germany)

Keywords: finite element method, hybrid modeling, neural operators, parametrized PDEs, reduced order modeling, scientific machine learning

MS008 Advances in machine learning and data-driven techniques for aerodynamic optimization and uncertainty quantification

Organized by: E. Andrés Pérez (INTA, Spain)

Keywords: machine learning; aerodynamics, uncertainty quantification

MS009 AI, Model Reduction and Data-Driven techniques for multiscale modelling of materials, structures and processes

Organized by: J. YVONNET (Université Gustave Eiffel, France), K. Weinberg (Universität Siegen, Germany), L. Stainier (Ecole Centrale Nantes, France) and M. Shakoor (Université Lille, France)

Keywords: approximation properties, digital models, material science, surrogate modeling

MS011 Minisymposium proposal: Machine Learning and AI in Multibody System Dynamics

Organized by: J. Gerstmayr (University of Innsbruck, Austria), D. Negrut (University of Wisconsin - Madison, United States), G. Orzechowski (Lappeenranta-Lahti University of Technology, Finland) and A. Zwölfer (Technical University Munich, Germany)

Keywords: computational methods, data-based methods, machine learning, multibody dynamics

MS013 Predictive data-driven model reduction and discovery for dynamical systems

Organized by: P. Conti (Politecnico di Milano, Italy), M. Guo (Lund University, Sweden), A. Frangi (Politecnico di Milano, Italy) and A. Manzoni (Politecnico di Milano, Italy)

Keywords: deep learning, digital twins, dynamical systems, Model discovery, neural networks, partial differential equations, reduced order modeling

MS014 Autoencoders for Fluid Mechanics and More

Organized by: E. Saetta (University of Naples Federico II, Italy), L. Magri (Imperial College London, United Kingdom), G. Rozza (SISSA, Italy) and G. Iaccarino (Stanford University, United States)

Keywords: Autoencoders, CFD, Fluid Machanics, reduced order modeling

MS016 Advancements of Data-Driven Methods in Computational Mechanics

Organized by: W. Sun (Columbia University, United States), J. Chen (University of California San Diego, United States), Q. He (University of Minnesota Twin Cities, United States) and N. Vlassis (Rutgers University, United States)

Keywords: causal discovery, denoising diffusion, inverse design, manifold learning, model-free approaches, Physics-Informed Machine Learning, representation learning

Digital Twins and Applications in Engineering, Infrastructure, and Sustainability

MS017 Digital twin technology for battery cell manufacturing

Organized by: F. Al Machot (Norwegian University of Life Sciences, Norway), S. Chiacchiera (UK Research and Innovation, United Kingdom), M. Horsch (Norwegian University of Life Sciences, Norway), M. Möckel (Aschaffenburg University of Applied Sciences, Germany), S. Stier (Fraunhofer Institute for Silicate Research, Germany), E. Sødahl (Norwegian University of Life Sciences, Norway), I. Todorov (UK Research and Innovation, United Kingdom) and E. Valseth (Norwegian University of Life Sciences, Norway)

Keywords: digital twins, industrial applications of AI, optimal experimental design, real-time monitoring

MS018 Digital Twins for Predictive Decision Making of Engineering Systems

Organized by: M. Torzoni (Politecnico di Milano, Italy), M. Tezzele (Emory University, United States) and A. Manzoni (Politecnico di Milano, Italy)

Keywords: data assimilation, digital twins, hybrid physics/AI approaches, real-time monitoring, reduced order modeling, scientific machine learning, surrogate modeling, uncertainty quantification

MS019 Digital Twins and Their Enabling Technologies for Infrastructures

Organized by: D. Wolff (University of the Bundeswehr Munich, Germany), M. von Danwitz (German Aerospace Center (DLR), Germany), T. Koch (German Aerospace Center (DLR), Germany) and A. Popp (University of the Bundeswehr Munich, Germany)

Keywords: digital twins, infrastructures, real-time monitoring, scientific machine learning, surrogate modeling

MS020 Digital twins of living systems: theoretical, implementation & application challenges

Organized by: M. Genet (École Polytechnique, France) and P. Moireau (École Polytechnique, France)

MS021 Digital Twins: Mathematical Engines and Applications for Sustainable Futures

Organized by: L. Mainini (Imperial College London, United Kingdom), M. Diez (Istituto di Ingegneria del Mare, Consiglio Na, Italy) and D. Quagliarella (Italian Aerospace Research Center, Italy)

Keywords: data assimilation, digital twins, inverse problems, real-time monitoring, scientific machine learning, sustainable futures, uncertainty quantification

MS023 Digital Twins for Infrastructures and Cities

Organized by: J. Bleyer (Ecole nationale des ponts et chaussées, France), M. Garcia Alberti (Universidad Politécnica de Madrid, Spain) and T. Lovas (Budapest University of Technology and Economi, Hungary)

Keywords: building information modeling, cities, geographic information systems, infrastructures, transportation, use cases

MS024 Digital Twins and AI-Enhanced Computational Methods for Structural Analysis

Organized by: N. Rosa (TEMA, DEM, University of Aveiro, Portugal), S. Tavares (TEMA, DEM, University of Aveiro, Portugal) and J. Belina (ISEP,Polytechnic University of Porto, Portugal)

Keywords: computational methods, digital twins AI, hybrid physics/AI approaches, uncertainty quantification

MS025 Mathematical Aspects of Machine Learning Methods in Computional Mechanics

Organized by: M. Larson (Umea University, Sweden) and D. Pardo (University of the Basque Country (UPV/EHU), Spain)

Keywords: approximation properties, error analysis, foundations of machine learning, neural operators, scientific machine learning, training, uncertainty quantification

MS026 Simplifying Digital Twin Development: Advances in Automation and AI

Organized by: J. Stürmer (German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Germany), T. Koch (German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Germany) and A. Popp (Institute for Mathematics and Computer-Based Simulation, University of the Bundeswehr Munich, Germany)

Keywords: digital twins

MS027 Successes and failures in scientific machine learning

Organized by: R. Maulik (Pennsylvania State University, United States), P. Stinis (Pacific Northwest National Laboratory, United States), H. Xiao (University of Stuttgart, Germany) and B. Sanderse (Centrum Wiskunde & Informatica, Netherlands)

Keywords: scientific machine learning

MS029 Optimal Control and Decision Making under Uncertainty from Digital Twins

Organized by: L. Chamoin (ENS Paris-Saclay, LMPS, France), L. Fribourg (CNRS, LMF, France), N. Mechbal (ENSAM Paris, France) and G. Stadler (New York University, United States)

Keywords: digital twins, dynamical systems, neural networks, optimal experimental design, partial differential equations, uncertainty quantification

MS030 Scientific Machine Learning and Uncertainty Quantification for Robust Digital Twins in Science and Engineering

Organized by: E. Chatzi (ETH Zurich, Switzerland), D. Giovanis (Johns Hopkins University, United States), D. Loukrezis (Siemens AG, Germany) and V. Papadopoulos (National Technical University of Athens, Greece)

Keywords: digital twins, hybrid modeling, scientific machine learning, uncertainty quantification

MS057 Digital Twins for Ship and Offshore Structures

Organized by: T. Kvamsdal (Norwegian University of Science and Technology, Norway)

Applications of Machine Learning in Medical and Healthcare Technologies

MS031 Digital Twins and Artificial Intelligence Approaches for Complex/Multiscale Physiological Systems in Oncology

Organized by: G. Lorenzo (University of A Coruña, Spain) and P. Zunino (Politecnico di Milano, Italy)

Keywords: Artificial Intelligence in Healthcare, Computational Oncology, Precision Medicine

MS033 Advanced and hybrid computational modeling (FEM, AI, MR) for evidence-based diagnosis in Healthcare

Organized by: M. HO BA THO (Université de technologie de Compiègne, France) and T. DAO (Université de Lille, Centrale Lille Institute, France)

Keywords: Artificial Intelligence in Healthcare, Computaional Modelling, digital twins, finite element method, hybrid physics/AI approaches

MS034 Model Order Reduction and Data Models in Medical Applications

Organized by: C. Ghnatios (University of North Florida, United States), R. Attieh (Mayo Clinic, United States), F. Panthier (Sorbonne Université, France), F. Chinesta (Arts et Métiers Institute of Technology, France) and C. Huber (Université Paris Descartes, France)

MS036 Machine-Learning based Model Order Reduction for Patient-specific computational modeling

Organized by: D. Ryckelynck (Mines Paris PSL, France) and P. Rohan (Arts et Métiers Institute of Technology, France)

Keywords: digital models, scientific deep learning, surrogate modeling

Mathematical Modeling, AI Techniques, and Multiphysics Problems

MS037 Integrating Scientific Machine Learning with Physics-Based Simulation F INDUSTRIAL MULTIPHYSICS APPLICATIONS

Organized by: F. De Vuyst (Université de Technologie de Compiègne, France), T. Dairay (Michelin, France), I. Mortazavi (CNAM Paris, France) and J. Berro-Ramirez (Altair Engineering France, France)

Keywords: benchmarks, digital twins, engineering design, error analysis, hybrid modeling, industrial applications of AI, parametrized PDEs, real-time monitoring, reduced order modeling, scientific machine learning, uncertainty quantification

MS038 Inverse Problems and Data Assimilation for Digital Twins

Organized by: R. White (Sandia National Laboratories, United States), T. Wildey (Sandia National Laboratories, United States) and J. Jakeman (Sandia National Laboratories, United States)

Keywords: data assimilation, digital twins, inverse problems, optimal experimental design

MS039 Deep Learning-based Reduced Order Models in Scientific Computing

Organized by: N. Franco (Politecnico di Milano, Italy), S. Fresca (Politecnico di Milano, Italy), C. Marcati (Università di Pavia, Italy) and F. Pichi (SISSA, Italy)

Keywords: approximation theory, neural networks, parametrized PDEs, reduced order modeling, scientific machine learning

MS040 Automatic Learning Of Constitutive Relations In Solid Mechanics

Organized by: L. Chamoin (LMPS, France), L. De Lorenzis (ETH Zürich, Switzerland), K. Kalina (TU Dresden, Germany), M. Kästner (TU Dresden, Germany), J. Fuhg (UT Austin, United States), K. Linka (RWTH Aachen, Germany), O. Weeger (TU Darmstadt, Germany) and E. Baranger (LMPS, France)

Keywords: data assimilation, digital models, PINNs, surrogate modeling, test cases

MS041 Mathematical modeling, numerical simulations and AI techniques to enhance battery lifetime

Organized by: D. Conte (University of Salerno, Italy), R. D'Ambrosio (University of L'Aquila, Italy) and I. Sgura (University of Salento, Italy)

Keywords: error analysis, foundation models for physical sciences, parametrized PDEs, PINNs, scientific machine learning

MS042 Generation and Exploitation of Experimental Full-Field Data in Solid Mechanics

Organized by: R. Bouclier (ICA, INSA-Toulouse, France), B. Blaysat (Institut Pascal, Université Clermont Auvergne, France), J. Passieux (ICA, INSA-Toulouse, France), J. Réthoré (GeM, Centrale Nantes, France), M. Geers (Eindhoven University of Technology, Netherlands) and K. Bhattacharya (5California Institute of Technology, United States)

Keywords: Experimental data analysis, full-field measurement techniques, hybrid physics/AI approaches, Image-based modeling, material identification, uncertainty quantification

MS043 Advancements of Physics-Informed Machine Learning in Modelling and Simulation for Engineering and Science

Organized by: Y. GU (School of Mechanical, Medical and Process Engineering, Australia), C. BATUWATTA-GAMAGE (School of Mechanical, Medical and Process Engineering, Australia) and H. JEONG (School of Mechanical, Medical and Process Engineering, Australia)

Keywords: Computaional Modelling, Engineering and Science, Physics-Informed Machine Learning

MS044 Neural Operators for PDEs in Complex Geometries

Organized by: O. Colomés (TU Delft, Netherlands) and A. Heinlein (TU Delft, Netherlands)

Keywords: Complex geometries, machine learning, neural operators

MS045 Discovering Evolution Equations Using Structure-Preserving Data-Driven Methods

Organized by: N. Trask (University of Pennsylvania, United States), S. Huang (Nanyang Technological University, Singapore) and B. Moya (CNRS@CREATE Ltd., Singapore)

Keywords: data-based methods, machine learning, structure-preserving model

MS046 Scientific Machine Learning Methodologies with Applications in Computational Mechanics

Organized by: A. Soulaimani (Azzeddine Soulaïmani, Professor, École de tec, Canada) and S. Prudhomme (Department of Mathematics and Industrial Engineering Polytechnique Montréal. C.P. 6079, succ. Centre-ville, Canada)

Keywords: Data-driven machine learning, forward problems, inverse problems, neural operators, phyiscs informed neural networs, training algorithms

MS047 AI improved computational methods for slender flexible structures

Organized by: E. Celledoni (NTNU, Norway)

Keywords: digital twins AI

MS048 Reduced order models for environmental applications in ocean and atmosphere

Organized by: J. Harris (École nationale des ponts et chaussées, France) and K. Kuznetsov (GRASP Earth, France)

Keywords: atmosphere, earth sciences, Fluid Machanics, ocean, reduced order modeling

MS049 Inverse Problems & Inverse Design

Organized by: L. Herrmann (Technical University of Munich, Germany), M. Kästner (TUD Dresden University of Technology, Germany) and S. Kollmannsberger (Bauhaus-University Weimar, Germany)

Keywords: inverse design, inverse problems, metamaterials, optimization, topology optimization

MS050 AI-Enhanced and HPC Methods for Challenging Computational Mechanics Applications

Organized by: I. Kalogeris (National Technical University of Athens, Greece), G. Sotiropoulos (National Technical University Of Athens, Greece), G. Arampatzis (University of Crete, Greece) and V. Papadopoulos (National Technical University of Athens, Greece)

Keywords: computational methods, HPC, machine learning, multi-physics simulations, surrogate modeling

MS051 Physics-Enhanced Machine Learning for Structural Health Monitoring

Organized by: A. Cicirello (University of Cambridge, United Kingdom), E. Cross (University of Sheffield, United Kingdom) and E. Chatzi (ETH Zurich, Switzerland)

Keywords: Data-driven machine learning, digital twins, dynamical systems, infrastructures, real-time monitoring, structural health monitoring

DTE AICCOMAS 2025

Minisymposia

Machine Learning, Data-Driven Approaches, and Scientific Computing in Engineering

Digital Twins and Applications in Engineering, Infrastructure, and Sustainability

Applications of Machine Learning in Medical and Healthcare Technologies

Mathematical Modeling, AI Techniques, and Multiphysics Problems