Description

Leader of the group Scientific Computing: Daan Crommelin.

The SC groups develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Such uncertainties arise from e.g. uncertain model parameters, chaotic dynamics or intrinsic randomness, and can have major impact on model outputs and predictions. Our work is targeted in particular at applications in climate, energy, finance and biology. In these vital areas, the ability to assess uncertainties and their impact on model predictions is of paramount importance. Expertise in the SC group includes uncertainty quantification, data assimilation, stochastic multiscale modeling and risk assessment. The availability of data to inform and improve simulations and predictions, for example through learning and data-driven modeling, plays an important role in our research.

 

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Vacancies

News

Current events

Workshop "Optimal asset allocation, recent developments in pension plans"

  • 2019-06-27T13:30:00+02:00
  • 2019-06-27T16:25:00+02:00
June 27 Thursday

Start: 2019-06-27 13:30:00+02:00 End: 2019-06-27 16:25:00+02:00

L120

Organized by Kees Oosterlee

13:30 -- 14:10  Prof.dr. Zili Zhu, Director of RiskLab Data61 of CSIRO  
(Commonwealth Scientific & Industrial Research Organisation of Australia)
Optimal Decisions-Making in Retirement Life-cycle Management
 
14:15 -- 14:45  Tim den Haan, Techn. Univ. Delft
On Dynamic Life Cycle pension plans

14:45 -- 15:15  Coffee, tea

15:15 -- 15:45  Dr. Martin van der Schans, Ortec Finance
Near Optimal Portfolio Construction

15:45 -- 16:25  Prof.dr. Duan Li, City Univ. Hong Kong
Mean-Variance Induced Utility Maximization Framework: Risk and Potential

 

Space is limited so please register for this workshop, thank you!
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Abstracts Talks 2 and 3:

On Dynamic Life Cycle pension plans, Tim den Haan

Abstract: This thesis studies the asset allocation of a DC pension investor over a long time horizon. Investors allocate their portfolio wealth between two assets: a return portfolio and a matching portfolio. Investors can adjust their allocation once a year. Several dynamic investment strategies that improve investment results compared to fixed allocations or static life cycles are shown. The dynamic investment strategies have been constructed by using two different approaches. The first approach is rule-based and defines intermediate wealth targets for every year in the investment horizon. Investment decisions are taken based on performance compared to these targets. The second approach involves a dynamic programming algorithm. The asset allocation over time is not always stable when using dynamic programming. Methods to smooth the asset allocation over time and improve stability are discussed. Last, both approaches are combined in one strategy.

Near-optimal portfolio construction, Dr. Martin van der Schans
 
Many investors use optimization to determine their optimal investment portfolio. Unfortunately, optimal portfolios are sensitive to changing input parameters, i.e., they are not robust. Traditional robust optimization approaches aim for an optimal and robust portfolio which, ideally, is the final investment decision. In practice, however, portfolio optimization supports but seldomly replaces the investment decision process. We present an approach that both solves the robustness problem and aims to support rather than replace the investment decision process. The method determines a region with near-optimal portfolios which, especially in light of the robustness problem, are all good allocation decisions. Then, as is already common practice, an investor can bring in expert opinion or additional information to select a preferred near-optimal portfolio. We will show that the region of near-optimal portfolios is significantly more robust than the optimal portfolio itself.
https://www.ortecfinance.com/en/insights/research/robust-optimization-by-constructing-near-optimal-portfolios
https://www.ortecfinance.com/en/insights/research/near-optimal-portfolio-construction

PhD Defense Prashant Kumar (SC)

  • 2019-07-16T10:00:00+02:00
  • 2019-07-16T12:00:00+02:00
July 16 Tuesday

Start: 2019-07-16 10:00:00+02:00 End: 2019-07-16 12:00:00+02:00

Senaatszaal of the Auditorium, Mekelweg 5 in Delft

You are cordially invited to the public defense of Prashant Kumar on his PhD thesis titled:

Multilevel Solvers for Stochastic fluid flows

Promotor: prof. dr. Cornelis W. Oosterlee (CWI, TU Delft)
Co-promotor: dr. Richard P. Dwight (TU Delft)

Members

Associated Members

Publications

Current projects with external funding

  • Accurate prediction of slugs in multiphase pipe flow simulation for improved oil and gas production
  • Towards cloud-resolving climate simulations
  • Valuation Adjustments for Improved Risk Management (ABC-EU-XVA)
  • Physics based ICT: The digital twin in pipelines (DP-Trans)
  • Excellence in Uncertainty Reduction of Offshore Wind Systems / Loads and Damage (EUROS)
  • Rare Event Simulation for Climate Extremes (RESClim)
  • Sloshing of Liquefied Natural Gas: subproject Variability (14-10-project2) (SLING)
  • Verified Exascale Computing for Multiscale Applications (VECMA)
  • WIND Turbine Rotor aeroelasticity UncErtainty quantification (WINDTRUE)

Related partners

  • DNV GL Netherlands B.V
  • FOM
  • Max Planck Institute for Informatics
  • Shell, Amsterdam
  • Bull Sas
  • CBK Sci Con Ltd
  • Bayerische Akademie der Wissenschaften
  • MeitY
  • Instytut Chemii Bioorganicznej Polskiej Akademii Nauk
  • Rijksuniversiteit Groningen
  • Suzlon Blades Technology
  • TNO
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft
  • Brunel University London
  • University College London
  • Universiteit van Amsterdam