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TU Delft Institute for Computational Science and Engineering (DCSE)

About DCSE

Computational Science and Engineering (CSE) is rapidly developing field that brings together applied mathematics, engineering and (social) science. DCSE is represented within all eight faculties of TU Delft. About forty research groups and more than three hundred faculty members are connected to, and actively involved in DCSE and its activities. Over 250 PhD students perform research related to computational science.

CSE is a multidisciplinary application-driven field that deals with the development and application of computational models and simulations. Often coupled with high-performance computing to solve complex physical problems arising in engineering analysis and design (computational engineering) as well as natural phenomena (computational science). CSE has been described as the "third mode of discovery" (next to theory and experimentation). In many fields, computer simulation, development of problem-solving methodologies and robust numerical tools are integral and therefore essential to business and research. Computer simulations provide the capability to enter fields that are either inaccessible to traditional experimentation or where carrying out traditional empirical inquiries is prohibitively expensive. 
 

Mission

DCSE’s mission is to take up a leading role in simulating and modelling physical, environmental and social phenomena using computational techniques. The institute is an authoritative community on computational science and engineering-related research, education and strategic cooperation. We are active within TU Delft and beyond: we focus on peers, industry, universities and knowledge institutes worldwide. 


Vision

As society, we face important social and engineering challenges. DCSE contributes to addressing these challenges. From a computational science point of view, four ingredients are crucially needed: (1) a deep and thorough understanding of underlying phenomena; (2) mathematical approximations, efficient algorithms, smart computer simulations and high-end computing resources; (3) a holistic approach to engineering, where people from different backgrounds and with different perspectives cooperate; (4) a well-trained and skilled ‘next generation’ of researchers, engineers and developers. 


Strategy

We connect all researchers who work on enabling technology for industry, and intensify cross-faculty research and cooperation. Our broad education portfolio geared towards the engineer of the future is designed for both bachelor, master, doctoral students, postdocs and staff. With the development of the Delft High Performance Computing Centre, we meet the enormous need among researchers and students for high-end computing resources. An emerging trend is a closer cooperation between CSE and AI, both in improved simulations and in the development of highly efficient algorithms that can be used in both CSE and AI. 
 

 

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News Archive

Agenda

23 april 2024 09:30 t/m 16:30

Command line and DelftBlue basics 23 April 2024

After taking this course, participants will be able to fully engage in remote HPC systems using the command line.

03 mei 2024 12:30 t/m 13:15

[NA] Jakob Zech: Nonparametric Distribution Learning via Neural ODEs

In this talk, we explore approximation properties and statistical aspects of Neural Ordinary Differential Equations (Neural ODEs). Neural ODEs are a recently established technique in computational statistics and machine learning, that can be used to characterize complex distributions. Specifically, given a fixed set of independent and identically distributed samples from a target distribution, the goal is either to estimate the target density or to generate new samples. We first investigate the regularity properties of the velocity fields used to push forward a reference distribution to the target. This analysis allows us to deduce approximation rates achievable through neural network representations. We then derive a concentration inequality for the maximum likelihood estimator of general ODE-parametrized transport maps. By merging these findings, we are able to determine convergence rates in terms of both the network size and the number of required samples from the target distribution. Our discussion will particularly focus on target distributions within the class of positive \(C^k\) densities on the \(d\)-dimensional unit cube \([0,1]^d\).

31 mei 2024 12:30 t/m 13:15

[NA] Hyea Hyun Kim: Partitioned neural network approximation to partial differential equations and its training performance enhancement utilizing domain decomposition algorithms

With the success of deep learning technologies in many scientific and engineering applications, neural network approximation methods have emerged as an active research area in numerical partial differential equations. However, the new approximation methods still need further validations on their accuracy, stability, and efficiency so as to be used as alternatives to classical approximation methods. In this talk, we first introduce partitioned neural network approximation to partial differential equations, where neural network functions localized in each small subdomains are employed as a solution surrogate in order to reduce the approximation and optimization errors in the standard single large neural network approximation. The parameters in each local neural network function are then optimized to minimize the corresponding cost function to the model problem. To enhance the parameter training efficiency further, iterative algorithms for the partitioned neural network function can be developed by utilizing classical domain decomposition algorithms and their convergence theory. We finally present promising features in this new approach as a way of enhancing the neural network solution accuracy, stability, and efficiency with some supporting numerical results.

12 juni 2024 09:30 t/m 16:30

High-performance computing using OpenMP 12 June 2024