Welcome to the group “Mathematics in Computational Science and Engineering (MATHICSE-Group)

The group of Mathematics in Computational Science and Engineering includes the Chair of Computational Mathematics and Numerical Analysis (ANMC), the Chair of Modelling and Scientific Computing (CMCS), the Chair of Numerical Algorithms and High Performance Computing (ANCHP), the Chair of Scientific Computing and Uncertainty Quantification (CSQI), the Chair of Computational Mathematics and Simulation Science (MCSS), the Chair of Numerical Modelling and Simulation (MNS) and the group of Professor M. Picasso (GR-PI).

Mission

Promote at the highest scientific level the research and education on mathematical modeling, numerical modeling, algorithmic development and simulation, as well as their application in nature, environment, life, society, science and engineering. Establish and lead research programs in Computational Science and Engineering within the Institute of Mathematics and interact with existing projects in the CSE area across the EPFL Campus.

EVENTS

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8:00

18:00

GA 3 21

Computational mathematics for model reduction and predictive modelling in molecular and complex systems

The workshop will address computational and theoretical issues in stochastic modeling and model reduction in molecular and complex systems. In particular, the program will include topics ranging from quantum to mesoscale modelling, with focus on uncertainty quantification, machine learning and approximate inference method, methods using both quantum and classical models, semiclassical limits and their computational aspect. Furthermore, the interplay between mathematical analysis, modeling and statistical physics and trade-offs between statistical (data-driven) learning and physicochemical modelling will be part of the discussions. The workshop aims at bringing together mathematicians and domain scientists interested in applications such as systems with excited states, empirical potentials, kinetic Monte Carlo methods and accelerated simulation methods determined from molecular dynamics, data assimilation and inference for predictive modeling of complex molecular systems.

Part of the Semester : Multi-scale Mathematical Modelling and Coarse-grain Computational Chemistry

By: Markos Katsoulakis, University of Massachusetts Amherst Benedikt Leimkuhler, University of Edinburgh Petr Pelchac, University of Delaware Anders Szepessy, KTH Royal Institute of Technology

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17:15

18:15

GA 3 21

Bernoulli Lecture - Efficient stochastic numerical methods for modelling and learning

 
Stochastic differential equations are fundamental tools of modern mathematical modelling; they describe everything from the performance of financial instruments to atomic motion to the complex motion of fish schools or bird flocks. In recent years they have also been used increasingly as tools for the parameterization of artificial models such as neural networks in their application to analysis of data sets. Given their great relevance, it is obviously of crucial importance to design numerical methods having high efficiency (i.e. accuracy and stability) for solving stochastic systems. I will describe a number of examples of stochastic differential equations arising in applications, while also presenting a few principles for the construction of high quality numerical methods for their computational treatment. Part of the Semester : Multi-scale Mathematical Modelling and Coarse-grain Computational Chemistry
 

By: Benedikt Leimkuhler (University of Edinburgh)

NEWS

Prof. Annalisa Buffa awarded the ERG Adcanced Grant CHANGE, “New CHallenges for (adaptive) PDE solvers: the interplay of ANalysis and Geometry”. Host Institution: Ecole Polytechnique Federale de Lausanne (EPFL). Addition Beneficiaries: Consiglio Nazionale Della Ricerche Istituto di Matematica Applicata e Technologie Informatiche “E. Magenes” (CNR-IMATI); Universitat Linz Johannes Kepler University (JKU-LINZ); Oesterreichische Akademie des Wissenschaften Johann Radon Institute for Computational and Applied Mathematics (RICAM)

ECCOMAS Award for the best PhD Thesis in 2016 to Dr D. Guignard (GR-PI & CSQI, EPFL)

Dr. Diane Guignard, member of the Chair of Scientific Computing and Uncertainty Quantification and the Group Picasso, Institute of Mathematics, EPFL, has been awarded the ECCOMAS Award for for one of the two best PhD Theses in 2016, for her PhD Thesis entitled “A posteriori error estimation for partial differential equations with random input data” (EPFL thesis #7260, 2016). The European Community on Computational Methods in Applied Sciences (ECCOMAS) attributes the award to highlight outstanding achievements of two young persons at the start of their scientific careers; the awards will be handed over at the ECCOMAS Young Investigator Conference – YIC 2017, Milan, Italy, September 13 – 15. PPP

Many congratulations to Diane!

Highlight on the Master in CSE

ECCOMAS Award for the best PhD Thesis in 2015 to Dr. F. Negri (CMCS, EPFL)

Dr. Federico Negri, member of the Chair of Modelling and Scientific Computing, MATHICSE, SB, EPFL, has been awarded the ECCOMAS Award for one of the two best PhD Theses in 2015. The European Community on Computational Methods in Applied Sciences (ECCOMAS) attributes the award to highlight outstanding achievements of two young persons at the start of their scientific careers; the award will be handed over at the ECCOMAS 2016 Conference in Crete, Greece, June 5 – 10.

The thesis of Dr. Negri, titled “Efficient Reduction Techniques for the Simulation and Optimization of Parametrized Systems: Analysis and Applications” (EPFL thesis #6810, 2015) and developed under the supervision of Prof. A. Quarteroni and Prof. G. Rozza, has been awarded the prize for its outstanding contributions in developing an efficient Reduced Basis method for the high fidelity solution of computationally-intensive problems described by Partial Differential Equations, with application to blood flows, mass transport, and control.

Many congratulations to Federico!