A5032487854- Geometric Analysis and Curvature Flows
- Reliability and Maintenance Optimization
- Geometry and complex manifolds
- Point processes and geometric inequalities
- Stochastic processes and financial applications
- Software Reliability and Analysis Research
- Advanced Optimization Algorithms Research
- Nonlinear Partial Differential Equations
- Assembly Line Balancing Optimization
- Smart Grid Energy Management
- Black Holes and Theoretical Physics
- Manufacturing Process and Optimization
- Distributed systems and fault tolerance
- Radio Astronomy Observations and Technology
- IoT and Edge/Fog Computing
- Advanced Multi-Objective Optimization Algorithms
- Statistical Distribution Estimation and Applications
- Reinforcement Learning in Robotics
- Energy, Environment, Agriculture Analysis
- Life Cycle Costing Analysis
- Probabilistic and Robust Engineering Design
- Capital Investment and Risk Analysis
- Optimization and Variational Analysis
- Sparse and Compressive Sensing Techniques
- Pharmaceutical Economics and Policy
Fraunhofer Chalmers Research Centre for Industrial Mathematics
2019-2024
Chalmers University of Technology
2012-2019
University of Gothenburg
2012-2019
We introduce the preventive maintenance scheduling problem with interval costs (PMSPIC), which is to schedule (PM) of components a system over finite and discretized time horizon, given common set-up cost component dependent on lengths intervals. present 0-1 integer linear programming (0-1 ILP) model for PMSPIC; identical that presented by Joneja (1990) joint replenishment within inventory management. study this from polyhedral exact solutions' point view, as opposed previously studied...
Abstract The planning and design of buildings civil engineering concrete structures constitutes a complex problem subject to constraints, for instance, limit state constraints from codes, evaluated by expensive computations such as finite element (FE) simulations. Traditionally, the focus has been on minimizing costs exclusively, while current trend calls good trade-offs multiple criteria sustainability, buildability, performance, which can typically be computed cheaply parameters....
The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order conduct transformational science. SKAO data products made available astronomers be correspondingly large and complex, requiring application of advanced analysis techniques extract key science findings. To this end, is conducting a series Science Data Challenges, each designed familiarise scientific community with drive development techniques. We present results from Challenge 2 (SDC2), which...
Dynamical systems of $N$ particles in $\Bbb{R}^{D}$ interacting by a singular pair potential mean field type are considered. The assumed to be gradient and the existence macroscopic limit many particle is established for large class interaction potentials stochastic as well deterministic settings. main assumption on an appropriate notion quasi-convexity. When $D=1$ convergence result sharp when applied strongly repulsive interactions general dimension $D$ applies attractive with Lipschitz...
In this paper, we consider online calibration of a Digital Twin and its use for control optimization in the assembly process sheet metal parts. This is done based on feedback signal received by calculating quality simulated assemblies as compared to prediction made Twin. We develop Kalman filter-based approach Twin, which turn used one-step look-ahead optimizer define an scheme. scheme balances directly predicted gains against reduced uncertainty whose purpose enable long-term gains. The...
Using economic model predictive control in space heating systems, the heat demand can flexibly be adapted to varying scenarios of conflicting objectives such as thermal comfort, energy consumption, and peak demand. With a controller that minimizes costs subject comfort constraints within occupancy hours, resulting will depend on cost mechanism. In context Swedish district networks, optimal side is multi-objective problem due variable based both consumption By simulating using gray-box...
Consider the utilization of a Lagrangian dual method which is convergent for consistent convex optimization problems. When it used to solve an infeasible problem, its inconsistency will then manifest itself through divergence sequence iterates. Will primal subproblem solutions still yield relevant information regarding program? We answer this question in affirmative program and associated subgradient algorithm Lagrange dual. show that primal–dual pair programs corresponding homogeneous...
Motivated by a probabilistic approach to Kahler-Einstein metrics we consider general non-equilibrium statistical mechanics model in Euclidean space consisting of the stochastic gradient flow given (possibly singular) quasi-convex N-particle interaction energy. We show that deterministic "macroscopic" evolution equation emerges large N-limit many particles. This is strengthening previous results which required uniform two-sided bound on Hessian The proof uses theory weak flows Wasserstein...
Context. The future deployment of the Square Kilometer Array (SKA) will lead to a massive influx astronomical data and automatic detection characterization sources therefore prove crucial in utilizing its full potential. Aims. We examine how existing knowledge tools can be utilized machine learning-based pipeline find 3D spectral line sources. Methods. present source-finding designed detect 21-cm emission from galaxies that provides second-best submission SKA Science Data Challenge 2. first...
In this paper we consider Monge–Ampère equations on compact Hessian manifolds, or equivalently certain unbounded convex domains in Euclidean space, with a periodicity constraint given by the action of an affine group. case where group is volume preserving, i.e., when manifold special, solvability corresponding equation was first established Cheng and Yau using continuity method. general set up variational framework involving dual manifolds generalization classical Legendre transform. We give...
Motivated by a probabilistic approach to Kähler-Einstein metrics we consider general nonequilibrium statistical mechanics model in Euclidean space consisting of the stochastic gradient flow given (possibly singular) quasiconvex N-particle interaction energy.We show that deterministic "macroscopic" evolution equation emerges large N-limit many particles.This is strengthening previous results which required uniform two-sided bound on Hessian energy.The proof uses theory weak flows Wasserstein...
Dynamical systems of N particles in \R^{D} interacting by a singular pair potential mean field type are considered. The assumed to be gradient and the existence macroscopic limit many particle is established for large class interaction potentials stochastic as well deterministic settings. main assumption on an appropriate notion quasi-convexity. When D=1 convergence result sharp when applied strongly repulsive interactions general dimension D applies attractive with Lipschitz potentials,...
Large pharmaceutical companies maintain a portfolio of assets, some which are projects under development while others on the market and generating revenue. The budget allocated to R&D may not always be sufficient fund all available for development. Much attention has been paid selection optimal subsets fit within budget. In this paper, we argue need forward-looking approach decision-making. We develop quantitative model that allows management evaluate future inflow new achieve revenue at...
In this paper we consider Monge-Amp\`ere equations on compact Hessian manifolds, or equivalently certain unbounded convex domains $\Omega\subseteq \mathbb{R}^n$, with a periodicity constraint given by the action of an affine group. case where group is volume-preserving, i.e., when manifold special, solvability corresponding equation was established using continuity method Cheng and Yau. general set up variational framework involving dual manifolds generalization classical Legendre transform....