Water flow generated by pillar in sinusoidal motion (from PhD research Yous van Halder) 

Scientific Computing (SC) enables the simulation of phenomena, processes and systems that cannot be studied by real experiments for technical, financial, safety or ethical reasons. SC also allows for automatic design and optimization, through inverse computations. Many disciplines in science and engineering have their own computational branches now. With continuing growth in speed, memory and cost-effectiveness of computers and similar improvements in numerical mathematics, the future benefits of SC are enormous.

Within CASA’s SC group, we propose, analyze, develop and implement new numerical mathematics methods, particularly structure-preserving discretization methods for partial differential equations and numerical linear algebra methods for linear systems of equations. We carry over these methods to Computational Science and Engineering (CSE), for application to particularly fluid-structure-interaction and energy-conversion problems.

Air vortices generated by impulsively started wind-turbine model blade (from PhD research René Beltman) 

Neural network developed for tokamak-plasma simulation (from MSc research Philipp Horn)
Traditionally, CSE is model-driven; based on mathematical models of first principles (physical laws for instance). Because of the immense growth in the availability of data, CSE is becoming data-driven as well, with an important role in this for neural networks. Within CASA’s SC group, neural-network technology in CSE is also studied. One of our research goals is to further develop neural networks in the context of CSE, combining data- and model-driven approaches, hand-in-hand with the development of more theory for a rational and trustworthy use of data and neural networks within CSE.

more publications…

  1. Sundaram V, Baumeier B. Dichotomous Initial Exciton Conversion at a DPP2Py2T-PCBM Interface: Insights from Embedded GW-Bethe–Salpeter Equation Calculations. Journal of Physical Chemistry B. 2026 Mar 26;130(12):3560-3570. doi: 10.1021/acs.jpcb.5c08651
  2. Zhou Q, Ren S, Baumeier B, Yi Z, Liu Y, Zhao Y. DPP-based terpolymers for enhanced electrical stretchability via backbone engineering toward delocalized frontier molecular orbital. Cell Reports Physical Science. 2026 Mar 18;7(3):103176. doi: 10.1016/j.xcrp.2026.103176
  3. Horn P, Saz Ulibarrena V, Koren B, Portegies Zwart S. A generalized framework of neural networks for Hamiltonian systems. Journal of Computational Physics. 2025 Jan 15;521:113536. doi: 10.1016/j.jcp.2024.113536
  4. Chen Z, van der Hoorn P, Baumeier B. A Graph Random Walk Method for Calculating Time-of-Flight Charge Mobility in Organic Semiconductors from Multiscale Simulations. Multiscale Modeling and Simulation. 2025 Dec;23(4):1439-1457. doi: 10.1137/24M1658085
  5. Koren B. A Journey in Scientific Computing. Eindhoven: Technische Universiteit Eindhoven, 2025. 32 p.
  6. Cai Y, Wei J, Hou Q, Fan H, Tijsseling AS. A Lagrangian particle model for one-dimensional transient pipe flow with moving boundary. Engineering Applications of Computational Fluid Mechanics. 2025;19(1):2452360. doi: 10.1080/19942060.2025.2452360
  7. Komen EMJ. An Assessment of the Collocated Grid Finite Volume Method for High-Fidelity CFD Simulations. Eindhoven: Eindhoven University of Technology, 2025. 205 p.
  8. Charles P, Ray D, Yu Y, Prins J, Melchers H, Abdelmalik MRA et al. An optimal Petrov-Galerkin framework for operator networks. arXiv.org. 2025 Mar 6. doi: 10.48550/arXiv.2503.04024
  9. Ferrandi G, Hochstenbach ME, Rosário Oliveira M. A subspace method for large-scale trace ratio problems. Computational Statistics and Data Analysis. 2025 May;205:108108. doi: 10.1016/j.csda.2024.108108
  10. Chen Z, van der Hoorn P, Baumeier B. Automatic identification of traps in molecular charge transport networks of organic semiconductors. Physical Review B. 2025 Jun 24;111(22):224209. doi: 10.1103/shyl-kcfp