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BIMoS Distinguished Lecture

For the BIMoS Distinguished Lecture an internationally recognized scientist in the general field of modeling and simulation is invited.

Mentiones BIMoS Distinguished Lecture takes place in room MA 001 (Mathematics building opposite the main building) of the TU Berlin in Straße des 17. Juni 136, 10623 Berlin.

Upcoming BIMoS Distinguished Lectures

Winter Term 2018/2019

Title: "Recent Theoretical and Computational Advances in the Optimization of Process Systems under Uncertainty"

Prof. Dr. Ignacio E. Grossmann (Carnegie Mellon University, Pittsburgh)

Thursday, 14 February 2019

4.15 pm - 6.00 pm


Optimization under uncertainty has been an active and challenging area of research for many years. However, its application in Process Systems has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust optimization vs. stochastic programming), handling of nonlinearities (most work addresses linear problems), large computational expense (orders of magnitude larger than deterministic models), and difficulty in the interpretation of the results by non-expert users.

In this lecture, we describe recent advances that address some of these barriers. We first describe the basic concepts of robust optimization, including the robust counterpart, showing its connections with semi-infinite programming. We also we explore the relationship between flexibility analysis and robust optimization for linear systems. A historical perspective is given, which shows that some of the fundamental concepts in robust optimization have already been developed in the area of flexibility analysis in the 1980s. We next consider two-stage and multi-stage stochastic programming in the case of exogenous parameter, for which we describe acceleration techniques for Benders decomposition, hybrid sub-gradient/cutting plane methods for Lagrangean decomposition, and sampling techniques. We address both mixed-integer linear and nonlinear stochastic programs, including integer recourse. We then address the generalization to the case of both exogenous and endogenous parameters, which gives rise to conditional scenario trees for which theoretical properties are described to reduce the problem size. To avoid ad-hoc approaches for setting up the data for these problems, we describe approaches for handling of historical data for generating scenario trees. Finally, we illustrate the application of each of these formulations in demand-side management optimization, planning of process networks, chemical supply chains under disruptions, planning of oil and gas fields, and optimization of process networks, all of them under some type of uncertainty.


Previous BIMoS Distinguished Lectures

Summer Term 2018

Title: "What Can Deep Learning Learn from Linear Regression "

Prof. Dr. Benjamin Recht (UC Berkeley, EECS)


Winter Term 2017/18

"The promise of Infrared Spectroscopy"

Prof. Dr. Chandrajit Bajaj (U Texas)


Summer Term 2017

"Mathematical Mysteries of Deep Neural Networks"

Prof. Dr. Stéphane Mallat (École Supérieure Normale)


Winter Term 2016/17

"Models, Scales, Data"

Prof. Dr. Wolfgang Dahmen (RWTH Aachen)


Summer Term 2016

"Navier-Stokes-Fokker-Planck systems: modelling, analysis and computation"

Prof. Dr. Endre Süli (U Oxford)


Winter term 2015/16

“Mathematical models for the cardiovascular system: numerical simulation, control and optimization, clinical applications”

Prof. Dr. Alfio Quarteroni (EPFL)

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BIMoS Office

Technische Universität Berlin
Berlin International Graduate School for Model and Simulation based Research (BIMoS)
MA 5-5
Straße des 17. Juni 136
10623 Berlin
+49 30 314 73620