mathematics

Jan S. Hesthaven

Jan S. Hesthaven is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Jan S. Hesthaven rather than just read about it. In short: Jan Sickmann Hesthaven (born 10 December 1965) is a Danish mathematician, currently president of the Karlsruhe Institute of Technology. He was previously Vice President for Academic Affairs (starting in 2021) at EPFL (École polytechnique fédérale de Lausanne) and held the Chair of Computational Mathematics and Simulation Science (MCSS).

Key takeaways

Jan S. Hesthaven belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Jan S. Hesthaven to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Jan S. Hesthaven from memory before moving on to harder problems.

Reference excerpt

Jan Sickmann Hesthaven (born 10 December 1965) is a Danish mathematician, currently president of the Karlsruhe Institute of Technology. He was previously Vice President for Academic Affairs (starting in 2021) at EPFL (École polytechnique fédérale de Lausanne) and held the Chair of Computational Mathematics and Simulation Science (MCSS). He is particularly known for contributions to the development, analysis and application of high-order accurate computational methods for time-dependent partial differential equations. He has also contributed substantially to the development of reduced order models and the application of neural networks and machine learning techniques to problems in science and engineering.

Career Prof. Hesthaven obtained a Master of Science degree in computational physics from the Technical University of Denmark (DTU) in 1991. In 1995, he received a Ph.D. in Numerical Analysis from the Institute of Mathematical Modelling (DTU) and in 2009 he was awarded the degree of Dr.Techn. from DTU for substantial and lasting contributions that has helped to move his research area forward and penetrated into applications. After graduation, he was appointed in 1995 as Visiting Assistant Professor at Brown University, then in 1999 as assistant professor and in 2003 he was promoted associate professor of applied mathematics with tenure at Brown University where in July 2005 he was promoted to professor of applied mathematics. In 2006, he founded the Center for Computation and Visualization (CCV) Archived 9 August 2020 at the Wayback Machine at Brown University and was its director until 2013. He served from Aug 2010 to June 2013 as founding deputy director of the Institute of Computational and Experimental Research in Mathematics ICERM, an NSF Mathematical Sciences Research Institute. In 2013, he joined EPFL (École polytechnique fédérale de Lausanne) where he was appointed full professor of computational mathematics and simulation science, and shortly later, in February 2014, he founded the new unit of Scientific IT and Application Support (SCITAS) From 2016 to 2021 he served as Editor in Chief of SIAM Journal of Scientific Computing. In February 2017, he became dean of the School of Basic Sciences (SB). In September 2020, he was appointed as Vice President for Academic Affairs at EPFL for a term starting in 2021. In January 2024, he was elected as the future president of the German Karlsruhe Institute of Technology (KIT) by the supervisory board. The election was confirmed by the Senate on 19 February 2024. He has assumed the presidency as of the 1st of October 2024.

Recognition Brown University gave him their Philip J. Bray Award for Teaching Excellence in 2004. He was elected as a SIAM Fellow in 2014. He was named to the 2023 class of Fellows of the American Mathematical Society, "for contributions to computational methods for PDEs, high-order accurate methods, and the reduced order method". In 2022 he was inducted as member of the Royal Danish Academy of Sciences and Letters. In 2023, he was elected as a member of Academia Europaea and the European Academy of Sciences. In 2024, the Technical University of Denmark awarded him an honorary doctorate in recognition of his scientific contributions

Books Hesthaven, Jan. (2007). Spectral methods for time-dependent problems. Gottlieb, Sigal., Gottlieb, David. Cambridge: Cambridge University Press. ISBN 978-0-511-26107-7. OCLC 162144521. Hesthaven, Jan S. (2008). Nodal discontinuous Galerkin methods : algorithms, analysis, and applications. Warburton, Tim. New York: Springer. ISBN 978-0-387-72067-8. OCLC 233971312. Hesthaven, Jan S. (2016). Certified reduced basis methods for parametrized partial differential equations. Rozza, Gianluigi, Stamm, Benjamin. Cham: Springer. ISBN 978-3-319-22470-1. OCLC 919086945. Hesthaven, Jan S. (2018). Numerical methods for conservation laws : from analysis to algorithms. Philadelphia: Society for Industrial and Applied Mathematics. ISBN 978-1-61197-509-3. OCLC 1012713065.

Notes and references

External links "Prof. Jan S Hesthaven's personal page". EPFL. Archived from the original on 27 October 2020. Retrieved 21 April 2020. "Prof. Jan S Hesthaven". LinkedIn. "Prof. Jan S Hesthaven". Google Scholar. Archived from the original on 22 August 2023. Retrieved 21 April 2020. "Prof. Jan S Hesthaven". Scopus.

Illustrations

Worked examples

Example 1 — a first encounter with Jan S. Hesthaven

Start with the simplest possible case. Write down what Jan S. Hesthaven claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Jan S. Hesthaven before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Jan S. Hesthaven ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Jan S. Hesthaven

In research: Jan S. Hesthaven appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Jan S. Hesthaven in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Jan S. Hesthaven is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1965 births, Academic staff of the Karlsruhe Institute of Technology, Academic staff of the École Polytechnique Fédérale de Lausanne, so understanding it makes those chapters shorter.
In everyday life: Look for Jan S. Hesthaven outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Jan S. Hesthaven in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Jan S. Hesthaven means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Jan S. Hesthaven out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Jan S. Hesthaven in simple terms?

Why does Jan S. Hesthaven matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Jan S. Hesthaven?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Jan S. Hesthaven.