NAME, title

4/2/20

Computational Engineering –Master Program

Aksel Hiorth, 30.10.2019

New Master Program 2019

Prof Steinar Evje, PhD in applied mathematics from UiB(Applied and Computational Mathematics, PDE)

Prof Reidar Bratvold (UiS),PhD in petroleum engineering from Stanford University (Bayesian Decision & Data Analytics, Value-of-Information)

Prof Aksel Hiorth, PhD in theoretical physics from UiO(Mathematical Modelling, multiphase flow and multi scale modelling)

”Combine engineering knowledge with modeling, decision making, and computational skills to gain a deeper understanding of real-world problems and to design the future solutions”

Dr. Aoije Hong and Dr. Oddbjørn NødlandPhD in Petroleum technology, UiS

New Master program 2019• Industry funding from Equinor Akademia

program

Key Personnel

Some feedback from companies

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«The future of petroleum engineers is exactly this; a combination of data, models and gathering information to make better decisions. This is a veryexciting program, and a good step in the right direction.»

Vibeke Haugen, Equinor

«Det er viktig å tenke langsiktig, og me ser eit behov for meir ingeniørfaglegkompetanse i helsesektoren. Dette gjeld spesielt innanfor modellering og programmering. Då kan arbeidstakarane sjølv bidra til å digitalisere og forbetre kvardagen»

Hilde Christiansen, direktør medarbeidar, organisasjon og teknologi, Helse Vest.

Student backgroundOpen for all engineering students:• A minimum of 10 ECTS in informatics or computer sciences, or an introductory

course for engineers including programming is required in addition to a minimum of 30 ECTS in mathematics and statistics.

A summer (before semester start up) course in python and basic computer stuff

Last year: students from data science, chemical-, electrical-, and petroleum engineering disciplines

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What is Modeling?

A model is a sufficient simplification of a real world problemModels have no value in and off themselves. They only have value to the extent theyare useful for some specific purpose. The main purpose of models is to providedecision supporting information.

Example: Modeling the spread of diseases How fast does a disease spread?How would different measures affect the spread?How would the economy be affected?

What is the best decision?

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Fall 2019: Modeling the Worst: A Zombie outbreak

No hope?: The Sokndal and Dirdal outbreak

WHO Ebola data

1. Sokndal outbreak: after 48 hours, the probability of being infected by azombie has dropped to 60% of its initial value.2. Dirdal outbreak: people are very tolerant to unorthodox behavior, whichis usually a good thing, but here it has the consequence that it takes 72hours before the probability of zombie infection drops to 60% of its initialvalue.

• Learn how to escape from a zombie threat using wisdom from simple compartment models.

• Learn how one can model the spreading of diseases mathematically.• Apply the model to study the outbreak of the Ebola virus in West-Africa in the

period 2014-2015.• Implement a selection of such models in Python, using standard methods.• Gain intuition about which parameters are important by performing model

sensitivity runs.

How can we model this phenomenon?

Same models that Imperial College and Folkehelseinstituttet use

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Compartment Model - SEIR

I

Recent data for COVID-19

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SEIR Model

«All models are wrong, but some are useful»

If we would like to know about human zombie interactions – the previous model is not useful

How can we model effect of measures?

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Zombies can be killed …

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Introduce walls as “quarantine”

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Modeling workflow

Understand the mechanisms•Match data• Predict the future

Make better decisions

G. E. Box

The only way you can purposefully influence your life, your family, your organization, your country, or your world is through the decisions you make.

BetterPerformance

BetterDecisions

Why the focus on decisions?

R. Bratvold

BetterPerformance

BetterDecisions

Commitmentto Action

Appropriate Frame

CreativeAlternatives

SoundReasoning

ClearObjectives & Tradeoffs

ReliableInformation

… but, decision quality is not only about data and information

R. Bratvold

Why Study Modeling?Models generate insights which lead to better decisions.We want to build models that are effective, that focus on what matters, and that are simple, clear, comprehensible, and correct.Modeling improves thinking skills: • Break problems down into components• Forces a focus on the specific elements of the problem that matter• Make assumptions explicit

Modeling improves quantitative skills:• Ballpark estimation, number sense, uncertainty thinking, sensitivity analysis

Modeling is widely used by analysts: • Engineering, geoscience, finance, operations

Copyright © 2013 John Wiley & Sons, Inc.17

Computational Engineering• Learning outcome

• A candidate with a completed master's degree in Computational Engineering from UiS will have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

• Knowledge:• Has advanced knowledge in the field of uncertainty quantification and modeling for decision support. Advanced

knowledge within uncertainty quantification and modeling for decision support means that you have the ability to develop mathematical models that account for uncertainties contained in incomplete data and information and provide the basis for improved understanding and interpretation of data as well as for decision support.

• Advanced knowledge of effective methods for designing, developing and testing models.• Advanced knowledge in the use of algorithms and computational thinking to solve discrete and continuous

problems.• Understand the limitations introduced by representing a complex system with a model. • Understand the constraints associated with the chosen solution method, including approximation errors and

constraints linked to the selection of specific algorithms or numerical methods.• Understand the importance of quantifying relevant and material uncertainties to generate insight and informed

decisions.• Deep understanding of the significance and consequences imbedded in the well-known quote: “All models are

wrong, but some models are useful ” (George E. Box).

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