A new approximate matrix factorization for implicit time integration in air pollution modeling

Implicit time stepping typically requires solution of one or several linear systems with a matrix I−τJ per time step where J is the Jacobian matrix. If solution of these systems is expensive, replacing I−τJ with its approximate matrix factorization (AMF) (I−τR)(I−τV), R+V=J, often leads to a good compromise between stability and accuracy of the time integration on the one hand and its efficiency on the other hand. For example, in air pollution modeling, AMF has been successfully used in the framework of Rosenbrock schemes. The standard AMF gives an approximation to I−τJ with the error τ2RV, which can be significant in norm. In this paper we propose a new AMF. In assumption that −V is an M-matrix, the error of the new AMF can be shown to have an upper bound τ||R||, while still being asymptotically $O(\tau^2)$. This new AMF, called AMF+, is equal in costs to standard AMF and, as both analysis and numerical experiments reveal, provides a better accuracy. We also report on our experience with another, cheaper AMF and with AMF-preconditioned GMRES

Improving approximate matrix factorizations for implicit time integration in air pollution modelling

For a long time operator splitting was the only computationally feasible way of implicit time integration in large scale Air Pollution Models. A recently proposed attractive alternative is Rosenbrock schemes combined with Approximate Matrix Factorization (AMF). With AMF, linear systems arising in implicit time stepping are solved approximately in such a way that the overall computational costs per time step are not higher than those of splitting methods. We propose and discuss two new variants of AMF. The first one is aimed at yet a further reduction of costs as compared with conventional AMF. The second variant of AMF provides in certain circumstances a better approximation to the inverse of the linear system matrix than standard AMF and requires the same computational work

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Human in the Loop: Interactive Passive Automata Learning via Evidence-Driven State-Merging Algorithms

We present an interactive version of an evidence-driven state-merging (EDSM) algorithm for learning variants of finite state automata. Learning these automata often amounts to recovering or reverse engineering the model generating the data despite noisy, incomplete, or imperfectly sampled data sources rather than optimizing a purely numeric target function. Domain expertise and human knowledge about the target domain can guide this process, and typically is captured in parameter settings. Often, domain expertise is subconscious and not expressed explicitly. Directly interacting with the learning algorithm makes it easier to utilize this knowledge effectively.Comment: 4 pages, presented at the Human in the Loop workshop at ICML 201

A Zooming Technique for Wind Transport of Air Pollution

In air pollution dispersion models, typically systems of millions of equations that describe wind transport, chemistry and vertical mixing have to be integrated in time. To have more accurate results over specific fixed areas of interest---usually highly polluted areas with intensive emissions---a local grid refinement or zoom is often required. For the wind transport part of the models, i.e.\ for finite volume discretizations of the transport equation, we propose a zoom technique that is positive, mass-conservative and allows to use smaller time steps as enforced by the CFL restriction in the zoom regions only

Geiten: Klim- en schuurmogelijkheden in de wei en in de stal

Op dit moment zijn er een aantal initiatieven en ideeën binnen de geitensector op het gebied van klim- en schuurmogelijkheden. Enkele voorbeelden zijn het gebruik van een (roterende) veeborstel,wandplanken in de stal en boomstammen in de weide. Klim- en schuurmogelijkheden worden echter nog niet op ieder bedrijf structureel toegepast. Barriëres om klim- en schuurmogelijkheden op het eigen bedrijf toe te passen zitten op gebied van arbeid, geld en praktische toepassing voor wat betreft bedrijfsvoering en bedrijfsgrootte. Naast de relevantie van klim- en schuurvoorzieningen in de stal en weide is ook aangegeven dat de mogelijkheid tot het schuilen in de stal en in de weide aandacht moet krijgen. Vanuit de sector wordt benadrukt dat de meest effectieve welzijnsverbeteringen gebaseerd zijn op aanpassingen/ maatregelen die dicht bij de natuur van de geit staan; gebruik de 'techniek van de natuur'

Familiekudde - natuurlijk houderijsysteem voor melkvee

De Familiekudde is een innovatief concept dat bijdraagt aan een duurzame (biologische) melkveehouderij. Veehouders uit het praktijknetwerk ‘Familiekudde’ staan op het punt om dit totaal nieuwe houderijsysteem toe te passen op hun bedrijf. In de Familiekudde ligt de nadruk op een natuurlijke leefomgeving. Het concept werkt met stabiele kuddes, kalveren bij de koe en zonder onthoornen. Het dier krijgt de ruimte en mogelijkheid om haar natuurlijke gedrag te vertonen. Er wordt rekening gehouden met bedrijfseconomische randvoorwaarden, milieu, diergezondheid en bedrijfsmanagement. In dit bioKennisbericht een introductie van het Familiekuddeconcept, de consequenties en oplossingen voor mogelijke knelpunten