Item 1 van 1

TU Delft Institute for Computational Science and Engineering (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. 

People

Research

Education

Events

Funding

Contact
Subscribe to our Newsletter

News

Martin van Gijzen aangesteld als de nieuwe wetenschappelijk directeur van het DHPC

Superrekenkracht voor racewagens en quantumtoestanden

1000th user for DelftBlue
News Archive

Agenda

11 december 2023 09:30 t/m 16:30

Command Line and DelftBlue Basics December 2023

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

15 december 2023 12:30 t/m 13:15

[NA] Stefan Kurz: Observers in relativistic electrodynamics

"We introduce a relativistic splitting structure to map fields and equations of electromagnetism from four-dimensional spacetime to three-dimensional observer's space. We focus on a minimal set of mathematical structures that are directly motivated by the language of the physical theory. Space-time, world-lines, time translation, space platforms, and time synchronization all find their mathematical counterparts. The splitting structure is defined without recourse to coordinates or frames. This is noteworthy since, in much of the prevalent literature, observers are identified with adapted coordinates and frames. Among the benefits of the approach is a concise and insightful classification of observers. The application of the framework to Schiff's ""Question in General Relativity"" [1] further illustrates the advantages of the framework, enabling a compact, yet profound analysis of the problem at hand. 

[1] Schiff, L. I. ""A question in general relativity."" Proceedings of the National Academy of Sciences 25.7 (1939): 391-395.
Consider two concentric spheres with equal and opposite total charges uniformly distributed over their surfaces. When the spheres are at rest, the electric and magnetic fields outside the spheres vanish. [...] Then an observer traveling in a circular orbit around the spheres should find no field, for since all of the components of the electromagnetic field tensor vanish in one coordinate system, they must vanish in all coordinate systems. On the other hand, the spheres are rotating with respect to this observer, and so he should experience a magnetic field. [...] It is clear in the above arrangement that an observer A at rest with respect to the spheres does not obtain the same results from physical experiments as an observer B who is rotating about the spheres."

19 januari 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 \([0,1]^d\).