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Space mapping

Publications associées (30)

A general and efficient framework for atomistic machine learning

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

EPFL2021

Optimization Methods for Control: From Embedded Programmable Hardware to Data-Driven Process Optimization

Harsh Ambarishkumar Shukla

The research community has been making significant progress in hardware implementation, numerical computing and algorithm development for optimization-based control. However, there are two key challenges that still have to be overcome for optimization-base ...

EPFL2021

Machine learning for metallurgy I. A neural-network potential for Al-Cu

William Curtin, Albert Johann Glensk, Daniel John Gilles Marchand, Abhinav Jain

High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic scale phenomena, particularly during ...

2020

Floating bridges and various methods for determining their long-term extreme response due to wave loading

This project presents the theoretical background for calculating the long-term extreme response of pontoon-style floating bridges. Research into surrogate models for the long-term extreme response is given special attention, and various simulations of the ...

2020

Accurate and efficient interpretation of load-test data for asset-management

Sai Ganesh Sarvotham Pai

Increasing demand for new infrastructure and ageing of existing infrastructure has made management of infrastructure a key challenge of this century. Replacement of all ageing civil infrastructure is economically and environmentally unsustainable. Civil in ...

EPFL2019

Aerodynamic shape optimization via surrogate modelling with convolutional neural networks

Francesco Bardi

Aerodynamic shape optimization has become of primary importance for the aerospace industry over the last years. Most of the method developed so far have been shown to be either computationally very expensive, or to have low dimensional search space. In thi ...

2019

Incorporating Deep Bagging Ensemble Method as a Surrogate Model for Simulating Hyper-Concentrated Sediment-Laden Flows

David Andrew Barry, Seyed Mahmood Hamze Ziabari

Macroscale and mesoscale simulations of hyper-concentrated sediment-laden flows rely on robust couplings of the Reynolds-Averaged Navier-Stokes equations in conjunction with the shear-stress transport k-ω turbulence model. Also other closure laws for model ...

2019

Leading the Blind

Grace Zgheib

The design and development of innovative FPGA architectures hinge on the flexibility of its toolchain. Retargetable toolchains, like the Verilog-to-Routing (VTR) flow, have been developed to enable the testing of new FPGAs by mapping circuits onto easily-d ...

EPFL2017

The GBS code for tokamak scrape-off layer simulations

Paolo Ricci, Joaquim Loizu Cisquella, Trach-Minh Tran, Sébastien Jolliet, Federico David Halpern, Fabio Riva, Annamaria Mosetto, Christoph Wersal

We describe a new version of GBS, a 3D global, flux-driven plasma turbulence code to simulate the turbulent dynamics in the tokamak scrape-off layer (SOL), superseding the code presented by Ricci et al. (2012) [14]. The present work is driven by the object ...

Elsevier2016

An Offline-Online Riemann Solver for One-Dimensional Systems of Conservation Laws

Alfio Quarteroni

In this paper, we present an exact Riemann solver for one-dimensional systems of conservation laws. The method is based on an offline-online computational decomposition. During the offline stage, we generate an accurate surrogate model for the solution to ...

Springer Singapore Pte Ltd2016

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