World's Best Scientists 2026 revealed!
Cornelis W. Oosterlee

Cornelis W. Oosterlee

Award Badge
Mathematics
Netherlands
2026

D-Index & Metrics

Mathematics

D-Index
48
Citations
9658
World Ranking
1203
National Ranking
9

Engineering and Technology

D-Index
47
Citations
9155
World Ranking
4823
National Ranking
99

Research.com Recognitions

  • 2026 - Research.com Mathematics in Netherlands Leader Award
  • 2025 - Research.com Mathematics in Netherlands Leader Award
  • 2023 - Research.com Mathematics in Netherlands Leader Award
  • 2022 - Research.com Mathematics in Netherlands Leader Award

Overview

Cornelis W. Oosterlee is affiliated with Utrecht University in the Netherlands. Their research primarily intersects the fields of Economics, Econometrics, and Finance, with a focus on topics that include stochastic processes and financial applications, model reduction and neural networks, financial risk and volatility modeling, credit risk and financial regulations, insurance, mortality, demography, and risk management, fluid dynamics and turbulent flows, as well as probabilistic and robust engineering design.

Oosterlee's scholarly output spans various publication venues where they have contributed multiple works. Frequent venues for their research include:

  • arXiv (Cornell University)
  • Applied Mathematics and Computation
  • SSRN Electronic Journal
  • Journal of Computational and Applied Mathematics
  • International Journal of Theoretical and Applied Finance

Key recent papers from Oosterlee and their coauthors reflect an emphasis on computational methods, neural networks, and applications to financial mathematics and pattern recognition. Selected recent works include:

  • "Optimally weighted loss functions for solving PDEs with Neural Networks," published in 2021 in the Journal of Computational and Applied Mathematics
  • "Optimally weighted loss functions for solving PDEs with Neural Networks," published in 2022 in Data Archiving and Networked Services (DANS)
  • "AIDA: Analytic isolation and distance-based anomaly detection algorithm," published in 2023 in Pattern Recognition
  • "A Probabilistic Digital Twin for Leak Localization in Water Distribution Networks Using Generative Deep Learning," published in 2023 in Sensors
  • "Financial option valuation by unsupervised learning with artificial neural networks," published in 2021 in Data Archiving and Networked Services (DANS)

Their collaboration network includes frequent coauthors such as Lech A. Grzelak, Pasquale Cirillo, Luis Antonio Souto Arias, Shuaiqiang Liu, and Nikolaj T. Mücke, indicating a multidisciplinary approach and engagement with diverse research communities.

The scientific topics addressed in Oosterlee's work show a strong orientation towards integrating advanced mathematical methods and neural network techniques into financial and engineering contexts. The range of subfields covered includes:

  • Finance
  • Statistical and Nonlinear Physics
  • Computational Mechanics
  • Management Science and Operations Research
  • Economics and Econometrics

Overall, Oosterlee's research portfolio demonstrates an emphasis on quantitative modeling, the application of computational algorithms to partial differential equations, machine learning methods, and their utilization in finance and risk management. This is reflected across a substantial number of publications and sustained collaboration within the research community.

Best Publications

  • A Novel Pricing Method for European Options Based on Fourier-Cosine Series Expansions

    F. Fang;C. W. Oosterlee

  • Conditional time series forecasting with convolutional neural networks

    Anastasia Borovykh;Sander Bohte;Cornelis W. Oosterlee

  • A Novel Multigrid Based Preconditioner For Heterogeneous Helmholtz Problems

    Y. A. Erlangga;C. W. Oosterlee;C. Vuik

  • On a class of preconditioners for solving the Helmholtz equation

    Y. A. Erlangga;C. Vuik;C. W. Oosterlee

  • A Fast and Accurate FFT-Based Method for Pricing Early-Exercise Options under Lévy Processes

    R. Lord;F. Fang;F. Bervoets;C. W. Oosterlee

  • Pricing early-exercise and discrete barrier options by fourier-cosine series expansions

    F. Fang;C. W. Oosterlee

  • On the Heston Model with Stochastic Interest Rates

    Lech A. Grzelak;Cornelis W. Oosterlee

  • Geometric multigrid with applications to computational fluid dynamics

    P. Wesseling;C. W. Oosterlee

  • Numerical valuation of options with jumps in the underlying

    Ariel Almendral;Cornelis W. Oosterlee

  • On multigrid for linear complementarity problems with application to American-style options.

    C.W. Oosterlee

  • A Fourier-Based Valuation Method for Bermudan and Barrier Options under Heston's Model

    Fang Fang;Cornelis W. Oosterlee

  • Pricing options and computing implied volatilities using neural networks

    Shuaiqiang Liu;Cornelis W. Oosterlee;Sander M. Bohte

  • Two-Dimensional Fourier Cosine Series Expansion Method for Pricing Financial Options

    Marjon Ruijter;Cornelis Oosterlee

  • Dilated convolutional neural networks for time series forecasting

    Anastasia Borovykh;Sander Bohte;Cornelis W. Oosterlee

  • Optimally weighted loss functions for solving PDEs with Neural Networks

    Remco van der Meer;Cornelis W. Oosterlee;Anastasia Borovykh

  • Comparison of multigrid and incomplete LU shifted-Laplace preconditioners for the inhomogeneous Helmholtz equation

    Y. A. Erlangga;C. Vuik;C. W. Oosterlee

  • Invariant discretization of the incompressible Navier‐Stokes equations in boundary fitted co‐ordinates

    A. Segal;P. Wesseling;J. Van Kan;C. W. Oosterlee

  • A neural network-based framework for financial model calibration

    Shuaiqiang Liu;Anastasia Borovykh;Lech A. Grzelak;Cornelis W. Oosterlee;Cornelis W. Oosterlee

  • A parallel multigrid-based preconditioner for the 3D heterogeneous high-frequency Helmholtz equation

    C. D. Riyanti;A. Kononov;Y. A. Erlangga;C. Vuik

  • Benchmark solutions for the incompressible Navier–Stokes equations in general co‐ordinates on staggered grids

    C.W. Oosterlee;P. Wesseling;A. Segal;E. Brakkee

  • Efficient Pricing of European-Style Asian Options under Exponential Lévy Processes Based on Fourier Cosine Expansions

    Bowen Zhang;Cornelis W. Oosterlee

  • Krylov Subspace Acceleration of Nonlinear Multigrid with Application to Recirculating Flows

    C. W. Oosterlee;T. Washio

  • THE HESTON STOCHASTIC-LOCAL VOLATILITY MODEL: EFFICIENT MONTE CARLO SIMULATION

    Anthonie W. Van Der Stoep;Lech A. Grzelak;Cornelis W. Oosterlee

Frequent Co-Authors

Cornelis Vuik
Cornelis Vuik Delft University of Technology
Wim Schoutens
Wim Schoutens KU Leuven
Ursula Keller
Ursula Keller ETH Zurich

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Pursuing a degree in Mathematics opens doors to numerous interdisciplinary fields. For those interested in data-driven decision-making, a masters data analytics program can be a natural extension. These programs build on mathematical principles and equip students with skills in statistics, machine learning, and big data management, essential for today’s tech-driven economy.

Alternatively, professionals aiming to enhance leadership and business acumen often explore graduate business degrees. Some students prioritize accessibility and flexibility, which is why researching the easiest mba programs can be beneficial. These programs maintain quality while offering a smoother admissions process and can serve as a bridge for careers combining math with management.

For those seeking online options with minimal barriers, the easy online mba courses provide a flexible pathway to gain business credentials without compromising academic rigour. These programs often cater to working professionals balancing education and careers.

Lastly, individuals aspiring to leadership roles in academia or industry may consider online dba programs. These Doctor of Business Administration degrees blend advanced research with practical business strategy, often emphasizing quantitative analysis, thereby complementing a strong mathematical foundation.

Best Scientists Citing Cornelis W. Oosterlee

Trending Scientists