World's Best Scientists 2026 revealed!
Matthias Heinkenschloss

Matthias Heinkenschloss

D-Index & Metrics

Mathematics

D-Index
38
Citations
4119
World Ranking
2404
National Ranking
1006

Engineering and Technology

D-Index
39
Citations
4196
World Ranking
7860
National Ranking
2146

Overview

Matthias Heinkenschloss is affiliated with Rice University in the United States. Their primary field of study is Engineering, with a focus on several specialized subfields including Control and Systems Engineering, Statistical and Nonlinear Physics, Numerical Analysis, Computational Mechanics, and Biomedical Engineering. Their research encompasses a range of topics emphasizing model reduction, advanced control systems, process optimization, numerical methods, microfluidic and catalytic innovations, as well as hydraulic and pneumatic systems.

The main topics of their scholarly work include:

  • Model Reduction and Neural Networks
  • Advanced Control Systems Optimization
  • Process Optimization and Integration
  • Numerical methods for differential equations
  • Innovative Microfluidic and Catalytic Techniques Innovation
  • Hydraulic and Pneumatic Systems
  • Nuclear Engineering Thermal-Hydraulics

Heinkenschloss has published multiple research papers across various reputable venues. Some of the notable recent papers authored or co-authored by them are:

  • "Adaptive Reduced-Order Model Construction for Conditional Value-at-Risk Estimation," 2020, SIAM/ASA Journal on Uncertainty Quantification
  • "A fast and accurate domain decomposition nonlinear manifold reduced order model," 2024, Computer Methods in Applied Mechanics and Engineering
  • "A fast and accurate domain-decomposition nonlinear manifold reduced order model," 2023, arXiv (Cornell University)

Other papers closely related through frequent co-authors include research on process intensification and energy savings in chemical engineering:

  • "Parastillation and metastillation applied to bioethanol and neutral alcohol purification with energy savings," 2021, Chemical Engineering and Processing - Process Intensification
  • "A new unified model to simulate columns with multiple phase divisions and their impact on energy savings," 2020, Computers & Chemical Engineering

Frequent collaborators of Matthias Heinkenschloss include Alejandro N. Diaz, Youngsoo Choi, Lílian Caroline Kramer Biasi, Fábio R.M. Batista, and Roger Josef Zemp. Their partnership has been reflected in numerous joint publications, particularly in applied mechanics and engineering computational methods.

Most of their publications appear in venues such as arXiv (Cornell University), Computer Methods in Applied Mechanics and Engineering, SIAM/ASA Journal on Uncertainty Quantification, Chemical Engineering and Processing - Process Intensification, and Computers & Chemical Engineering.

Best Publications

  • Large-Scale PDE-Constrained Optimization: An Introduction

    Lorenz T. Biegler;Omar Ghattas;Matthias Heinkenschloss;Bart van Bloemen Waanders

  • Large-Scale Inverse Problems and Quantification of Uncertainty

    Lorenz Biegler;George Biros;Omar Nabih Ghattas;Matthias Heinkenschloss

  • Shape optimization in steady blood flow: a numerical study of non-newtonian effects

    Feby Abraham;Marek Behr;Matthias Heinkenschloss

  • Trust-Region Interior-Point SQP Algorithms for a Class of Nonlinear Programming Problems

    J. E. Dennis;Matthias Heinkenschloss;Luís N. Vicente

  • Balanced Truncation Model Reduction for a Class of Descriptor Systems with Application to the Oseen Equations

    Matthias Heinkenschloss;Danny C. Sorensen;Kai Sun

  • Large-Scale PDE-Constrained Optimization

    Lorenz T. Biegler;Matthias Heinkenschloss;Omar Ghattas;Bart van Bloemen Waanders

  • Analysis of Inexact Trust-Region SQP Algorithms

    Matthias Heinkenschloss;Luis N. Vicente

  • A TRUST-REGION ALGORITHM WITH ADAPTIVE STOCHASTIC COLLOCATION FOR PDE OPTIMIZATION UNDER UNCERTAINTY ∗

    D. P. Kouri;M. Heinkenschloss;D. Ridzal;B. G. van Bloemen Waanders

  • Analysis of the Streamline Upwind/Petrov Galerkin Method Applied to the Solution of Optimal Control Problems ∗

    S. Scott Collis;Matthias Heinkenschloss

  • Real-Time PDE-Constrained Optimization

    Lorenz T. Biegler;Omar Ghattas;Matthias Heinkenschloss;David Keyes

  • Superlinear and quadratic convergence of affine-scaling interior-point Newton methods for problems with simple bounds without strict complementarity assumption

    Matthias Heinkenschloss;Michael Ulbrich;Stefan Ulbrich

  • A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

    Matthias Heinkenschloss

  • Brief paper: Balanced truncation model reduction for systems with inhomogeneous initial conditions

    M. Heinkenschloss;T. Reis;A. C. Antoulas

  • Preconditioners for Karush-Kuhn-Tucker Matrices Arising in the Optimal Control of Distributed Systems

    A. Battermann;M. Heinkenschloss

  • Inexact Objective Function Evaluations in a Trust-Region Algorithm for PDE-Constrained Optimization under Uncertainty.

    D. P. Kouri;M. Heinkenschloss;D. Ridzal;B. G. van Bloemen Waanders

  • Numerical Solution of Implicitly Constrained Optimization Problems

    Matthias Heinkenschloss

  • Global Convergence of Trust-region Interior-point Algorithms for Infinite-dimensional Nonconvex Minimization Subject to Pointwise Bounds

    Michael Ulbrich;Stefan Ulbrich;Matthias Heinkenschloss

  • Analysis of the Lagrange-SQP-Newton Method for the Control of a Phase Field Equation

    M. Heinkenschloss;F. Troeltzsch

  • Formulation and Analysis of a Sequential Quadratic Programming Method for the Optimal Dirichlet Boundary Control of Navier-Stokes Flow

    Matthias Heinkenschloss

  • Local Error Estimates for SUPG Solutions of Advection-Dominated Elliptic Linear-Quadratic Optimal Control Problems

    Matthias Heinkenschloss;Dmitriy Leykekhman

  • A Trust-Region Algorithm with Adaptive Stochastic Collocation for PDE Optimization under Uncertainty.

    Denis Ridzal;Bart G. van Bloemen Waanders;Drew P. Kouri;Matthias Heinkenschloss

Frequent Co-Authors

Danny C. Sorensen
Danny C. Sorensen Rice University
Omar Ghattas
Omar Ghattas The University of Texas at Austin
Lorenz T. Biegler
Lorenz T. Biegler Carnegie Mellon University
Athanasios C. Antoulas
Athanasios C. Antoulas Rice University
Karen Willcox
Karen Willcox The University of Texas at Austin
Peter Benner
Peter Benner Max Planck Institute for Dynamics of Complex Technical Systems
David E. Keyes
David E. Keyes King Abdullah University of Science and Technology
John E. Dennis
John E. Dennis Rice University
Luís Nunes Vicente
Luís Nunes Vicente Lehigh University
Antonio J. A. Meirelles
Antonio J. A. Meirelles State University of Campinas

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

For students studying Mathematics in the USA, exploring related online degrees can open diverse career pathways. Many graduates consider advancing their education with business-focused programs, which complement analytical and quantitative skills gained from mathematics.

If you're wondering what mba programs can i get into, numerous options exist with varying admission requirements, including some institutions known for being more accessible to applicants. This flexibility allows math majors to strengthen management and leadership skills efficiently.

Those seeking flexibility might explore the easiest online mba options. These programs suit professionals balancing work and studies and are often designed to enhance career prospects in sectors like finance and data analysis.

For those interested in specialized leadership roles, researching the most affordable online dba programs can provide pathways to doctoral-level expertise in business administration at a manageable cost.

Additionally, an online masters in finance is an excellent choice for math graduates aiming to enter finance-intensive careers, combining strong mathematical foundations with financial theory and applications.

Best Scientists Citing Matthias Heinkenschloss

Trending Scientists