Konstantinos Karapiperis
EPFL ENAC IIC LMD
GC B2 504 (Bâtiment GC)
Station 18
1015 Lausanne
+41 21 693 14 23
Office: GC D2 407
EPFL › ENAC › IIC › LMD
Website: https://www.epfl.ch/labs/lmd/
Kostas Karapiperis is an Assistant Professor at EPFL directing the Data-Driven Mechanics Laboratory (LMD). His research lies at the intersection of mechanics, multiscale modeling, and data science, with applications ranging from geomaterials to structural materials. Prior to EPFL, he worked as a Postdoctoral Researcher and Lecturer at the Department of Mechanical and Process Engineering of ETH Zürich, supported by a Marie Sklodowska-Curie Fellowship. He holds a PhD in Applied Mechanics and a Minor in Applied Mathematics from Caltech.
2025
Journal Articles
Topology-informed design of intertwined architected materials: Unifying woven, knotted, and closed-chain networks
Materials & Design. 2025. DOI : 10.1016/j.matdes.2025.114974.t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials
Journal of the Mechanics and Physics of Solids. 2025. DOI : 10.1016/j.jmps.2025.106049.Deciphering necking in granular materials: Micromechanical insights into sand behavior during cycles of triaxial compression and extension
Journal of the Mechanics and Physics of Solids. 2025. DOI : 10.1016/j.jmps.2024.106022.Enhanced Deformability Through Distributed Buckling in Stiff Quasicrystalline Architected Materials
Advanced materials (Deerfield Beach, Fla.). 2025. DOI : 10.1002/adma.202505125.Conference Papers
AI-Accelerated Multiscale Modeling
2025. XVIII International Conference on Computational Plasticity (COMPLAS 2025), Barcelona, Spain, 2025-09-02 - 2025-09-05.Sand Behavior upon Alternating Triaxial Compression and Extension: A Multiscale Analysis Using LS-DEM
2025. 5th International Symposium on Geomechanics from Micro to Macro, Grenoble, France, 2024-09-23 - 2024-09-27. DOI : 10.1088/1755-1315/1480/1/012028.2024
Journal Articles
Nonlinear two-scale beam simulations accelerated by thermodynamics-informed neural networks
Extreme Mechanics Letters. 2024. DOI : 10.1016/j.eml.2024.102260.A conforming frictional beam contact model
Computer Methods in Applied Mechanics and Engineering. 2024. DOI : 10.1016/j.cma.2024.117310.Failure of topologically interlocked structures — a Level-Set-DEM approach
European Journal of Mechanics, A/Solids. 2024. DOI : 10.1016/j.euromechsol.2023.105156.Book Chapters
Data-Driven Modeling in Geomechanics
Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-based Artificial Neural Networks and Reinforcement Learning; Wiley, 2024. p. 1 - 23.Working Papers
A discretization-convergent Level-Set-DEM
2024Stiff and Deformable Quasicrystalline Architected Materials
20242023
Journal Articles
Prediction and control of fracture paths in disordered architected materials using graph neural networks
Communications Engineering. 2023. DOI : 10.1038/s44172-023-00085-0.Unifying the design space and optimizing linear and nonlinear truss metamaterials by generative modeling
Nature communications. 2023. DOI : 10.1038/s41467-023-42068-x.A discretization‐convergent level‐set‐discrete‐element‐method using a continuum‐based contact formulation
International Journal for Numerical Methods in Engineering. 2023. DOI : 10.1002/nme.7400.A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Advanced Engineering Materials. 2023. DOI : 10.1002/adem.202300947.Data-driven breakage mechanics: Predicting the evolution of particle-size distribution in granular media
Journal of the Mechanics and Physics of Solids. 2023. DOI : 10.1016/j.jmps.2023.105328.Scaling, saturation, and upper bounds in the failure of topologically interlocked structures
International Journal of Solids and Structures. 2023. DOI : 10.1016/j.ijsolstr.2023.112228.Adaptive goal-oriented data sampling in Data-Driven Computational Mechanics
Computer Methods in Applied Mechanics and Engineering. 2023. DOI : 10.1016/j.cma.2023.115949.2022
Journal Articles
What is shape? Characterizing particle morphology with genetic algorithms and deep generative models
Granular Matter. 2022. DOI : 10.1007/s10035-022-01282-y.Research
Keywords
Solid Mechanics
Multiscale Modeling
Machine Learning
Geomechanics
Fracture Mechanics
Granular Materials
Architected materials
Multiscale Modeling
Machine Learning
Geomechanics
Fracture Mechanics
Granular Materials
Architected materials
Teaching & PhD
Current Phd
Hrishikesh Gopakumar Menon, Thomas Henzel
Courses
Multiscale modelling in mechanics
This course introduces the principles and techniques for modeling materials across different spatial scales, from the level of atoms or grains to the continuum or structural scale. Emphasis is placed on hierarchical upscaling (homogenization), while concurrent techniques are also covered.
Soil mechanics and flow through porous media
The course introduces students to the fundamentals of the mechanics of soils and fluid-infiltrated porous media. The first part is devoted to the constitutive behavior of soils in dry conditions, while the second part accounts for the presence of pore water.