JACQUELINE CHEN

Sandia National Laboratories, USA

The convergence of exascale computing and data science towards zero-carbon fuels for power and transportation.

CHARBEL FARHAT

Stanford University, USA

Multidisciplinary Computational Sciences for Landing on Mars

ALFIO MARIA QUARTERONI

Politecnico di Milano, Italy and EPFL, Lausanne, Switzerland

Physics based models and data driven algorithms for the simulation of the heart function.

NIKOLAUS ADAMS

Munich Institute of Integrated Materials, Energy, and Process Engineering, Germany

Beyond super-resolution – effective numerical sampling of complex fluid flow

YURI BAZILEVS

Brown University, USA

Stabilized and Multiscale Methods: Unifying CFD for Science and Engineering

RAMON CODINA

Universitat Politècnica de Catalunya, Spain

Stabilization and accuracy enhancement using artificial neural networks for reduced order models in flow problems

ANTONIO HUERTA

Universitat Politècnica de Catalunya, Spain

Face-centered solvers for robust CFD simulations

DAVID LE TOUZÉ

École Centrale de Nantes, France

Modeling complex free-surface flow with the Smoothed Particle Hydrodynamics method, from theory to application.

SIMONA PEROTTO

Politecnico di Milano, Italy

Innovative design of structures and materials: multi-objective, multi-scale and multi-physics scenarios

ANNE-VIRGINIE SALSAC

Université de Technologie de Compiègne, France

Fluid-structure interactions of liquid-core microcapsules in flow.

SPENCER SHERWIN

Imperial College of London, UK

Advancing spectral/hp element high fidelity simulation of incompressible and compressible flows

IRENE VIGNON-CLEMENTEL

INRIA, France

Blood flow simulations for disease and surgical treatment understanding

WOLFGANG A. WALL

Institute for Computational Mechanics & Center for Computational Biomedical Engineering Technical University of Munich (TUM), Germany

Potential of Probabilistic Thinking and Supercomputing for (Coupled) Computational Fluid Dynamics

KAREN E. WILLCOX

The University of Texas at Austin, USA

Nonlinear manifold approximations for reduced-order modeling of nonlinear systems

MATTHEW ZAHR

University of Notre Dame, USA

High-Order Implicit Shock Tracking for High-Speed Flows