Modeling, Simulation and Optimization of Complex Processes HPSC 2018: Proceedings of the 7th International Conference on High Performance Scientific Computing, Hanoi, Vietnam, March 19-23, 2018 303055239X, 9783030552398

Table of contents :
Preface
Contents
Global Optimization Approach for the Ascent Problem of Multi-stage Launchers
1 Introduction
2 Problem Statement. Mathematical Formulation
2.1 Physical Model
2.2 Axis, Angles, and Forces
2.3 Motion's Equations
2.4 The Flight's Phases
2.5 Optimal Control Problem
3 Global Optimization Approach
3.1 A Global Optimization Procedure
3.2 Properties of the Minimal Time Function mathcalT. Existence of solutions for the inner problem (11)
3.3 The HJB Equation
3.4 Numerical Approximation Of
3.5 Analytic Expression of the Hamiltonian Function
3.6 Optimal Trajectory Reconstruction Procedure
4 Numerical Simulations
4.1 Approximation of the Set X0
4.2 Numerical Computation of the Minimal Time Function mathcalT
4.3 Sensitivity Analysis with Respect to the Initial Parameters
4.4 Numerical Results
4.5 Conclusion and Future Work
References
A Robust Predictive Control Formulation for Heliogyro Blade Stability
1 Introduction
2 Control Formulation
2.1 Stability Considerations
2.2 Robustness
3 Heliogyro Attitude Dynamics
3.1 Position and Velocity Formulation
3.2 Energy Formulation
3.3 SRP Forcing and Linearization
4 HELIOS Control Strategy
5 Analysis
5.1 Model Size
5.2 Sensor Layout
5.3 Application onto Nonlinear System
6 Conclusions
References
Piecewise Polynomial Taylor Expansions—The Generalization of Faà di Bruno's Formula
1 Introduction, Preliminaries and Notions
2 Propagation Scheme of Expansions for Non-smooth Evaluation Procedures
3 Connections to Splines
4 The Generalization of Taylor's Theorem
5 A Generalized Integrator for Semi-explicit DAEs
6 Conclusion and Outlook
References
Grid-Enhanced Polylithic Modeling and Solution Approaches for Hard Optimization Problems
1 Introduction
2 Literature Review
3 Mathematical Structure of the Grid Approach
4 Optimization Problems and Optimization Algorithms Suitable for The Grid Approach
5 IT-Aspects and Implementation
5.1 Generic Structure
5.2 Implementation in GAMS
5.3 Module Mg
6 Real World Examples
6.1 Cutting Stock Pareto Front
6.2 Scheduling Problem in the Process Industry
6.3 2D Minimal Perimeter Convex Hulls
6.4 3D Minimal Surface Area Convex Hulls
7 Test Suite of Data Instances
8 Conclusions and Discussions
References
Model Predictive Q-Learning (MPQ-L) for Bilinear Systems
1 Introduction
2 Model Predictive Control
2.1 Design 1: Zero r-Step Ahead State
2.2 Design 2: Input-Weighted r-Step Ahead State
2.3 Design 3: Finite-Duration Receding Cost
2.4 Finding a State Feedback Control Law
3 The Q-Function in Q-Learning
4 Q-Function Computed from System Model and Controller
5 A Representation of the Q-Function
6 Updating the Q-Function via a Recurrence Equation
7 Model Predictive Q-Learning Algorithm (MPQ-L)
8 An Illustrative Example
8.1 MPC-Based Design
8.2 Q-Learning
8.3 Verification of Optimality
9 Discussion
References
SCOUT: Scheduling Core Utilization to Optimize the Performance of Scientific Computing Applications on CPU/Coprocessor-Based Cluster
1 Introduction
2 Background
2.1 Heterogeneous System—CPU/Coprocessor Cluster
2.2 Resources Over-Subscription
2.3 Thread Affinity
3 Related Works
4 Problem Definition and Motivation
5 SCOUT Model and Implementation
6 Experimental Results
6.1 Testing Environment and Benchmarks
6.2 Performance
7 Conclusions and Future Work
References
Chainer-XP: A Flexible Framework for ANNs Run on the Intel® Xeon PhiTM Coprocessor
1 Introduction
2 Deep Learning Overview
3 pyMIC
4 Chainer-XP
5 Evaluation
5.1 Environmental Setup
5.2 Experimental Results
6 Related Work
7 Conclusions and Future Work
References
Inverse Problems in Designing New Structural Materials
1 Introduction
2 Predictor Function and Forward Operator
3 Inverse Problem
3.1 Choosing the Discrepancy Term
3.2 Reducing Ill-Posedness
3.3 Optimization
4 Fully Learned Inverse
5 Numerical Results
6 Further Work
7 Summary and Conclusions
References
Coupled Electromagnetic Field and Electric Circuit Simulation: A Waveform Relaxation Benchmark
1 Introduction
2 Electric Circuits
2.1 Matrix Representation of Circuit Structures
2.2 Lumped Modeling of Circuits
2.3 Analysis of the Lumped Circuit DAE
3 Electromagnetic Devices
3.1 Modeling
4 Coupled Electric Circuits and Electromagnetic Devices
4.1 Modeling
4.2 Systems
5 Waveform Relaxation Method
5.1 Gauss–Seidel Method
5.2 Convergence Analysis
5.3 Benchmark
6 Conclusion and Outlook
References
SCIP-Jack: An Exact High Performance Solver for Steiner Tree Problems in Graphs and Related Problems
1 Introduction
1.1 Notation and Preliminaries
1.2 SCIP-Jack: A High Level View
2 Steiner Tree Problems in Graphs
2.1 The PACE Challenge
2.2 Recent Developements and Further Results
3 Maximum-Weight Connected Subgraph Problems
4 Hop-Constrained Steiner Tree Problems
5 Further Related Problems
5.1 Prize-Collecting Steiner Tree Problems
5.2 Node-Weighted Steiner Tree Problems
5.3 Group Steiner Tree Problems
5.4 Rectilinear Steiner Tree Problems
5.5 Degree-Constrained Steiner Tree Problems
6 Ug[SCIP-Jack,*]: Shared and Distributed Parallelization
7 Outlook: There and Back Again
References
Physical Parameter Identification with Sensors or Actuators Spanning Multiple DOF's
1 Introduction
2 System Models and Problem Statement
3 Preliminary Relationship for the Partitions of T
4 Additional Linear Equations to Find T1 for a System Without a Full Set of Sensors
5 Finding T1 for Different Types of Measurements
5.1 Case 1 : Displacement Measurements
5.2 Case 2 : Velocity Measurements
5.3 Case 3 : Acceleration Measurements
6 Step-by-Step Algorithm
7 Numerical Illustration
7.1 Case 1 : Displacement Measurements
7.2 Case 2 : Velocity Measurements
7.3 Case 3 : Acceleration Measurements
8 Conclusions
References
Monotonization of a Family of Implicit Schemes for the Burgers Equation
1 Introduction
2 One-Dimensional Problem
3 Numerical Monotonization
3.1 Spatial Mesh Refinement
3.2 Enlarging Time-Step Size
3.3 Increasing the Order of Spatial Approximation
4 Two-Dimensional Problem
5 Concluding Remarks
References
The Insensitivity of the Iterative Learning Control Inverse Problem to Initial Run When Stabilized by a New Stable Inverse
1 Introduction
2 The True Inverse Solution Producing Zero Tracking Error at Every Time Step
3 Iterative Learning Control Laws
4 Addressing Instability and Ill-Conditioning of the Inverse Problem
4.1 Instability of the Inverse Problem
4.2 Addressing the Instability in the Inverse Problem
4.3 Viewing the Ill-Conditioning and Instability in Terms of the Singular Value Decomposition of Matrix P
5 Making ILC Converge to a Stable Inverse
5.1 Behavior of ILC When Solving the Ill-Conditioned Problem
5.2 Modifying ILC Laws to Aim for Stable Inverse
6 Analytical and Numerical Results
6.1 The γ Parameter Set of All Possible Solutions to the Underspecified Equations
6.2 The γ Values for Each ILC Law as a Function of the Initial ILC Run
6.3 The Influence of the Initial Run on the Converged Final Control History
6.4 The Influence of the Initial Run on the Converged Error of the Unaddressed First Time Step
7 Conclusions
References
Strategy Optimization in Sports via Markov Decision Problems
1 Introduction
2 Sport Strategy Optimization and MDPs
3 The New Two-Scale MDP Approach
4 A Strategic MDP for Beach Volleyball
5 Computational Results I
6 A Gameplay MDP for Beach Volleyball
7 Gameplay MDP Strategy
8 Gameplay MDP Validation
9 Computational Results II
10 Comparison of Methods
11 Sensitivity and Skill Strategy Score Cards
12 Extension: Two Person Constant Sum Game
13 Conclusion
References
An Application of RASPEN to Discontinuous Galerkin Discretisation for Richards' Equation in Porous Media Flow
1 Introduction
2 RASPEN
2.1 Subdomain Solves
2.2 Preconditioned Nonlinear System
2.3 Newton Iteration
2.4 Algorithm
3 Numerical Experiments
3.1 P-Laplace Equation
3.2 Richards' Equation
4 Conclusion
References
On the Development of Batch Stable Inverse Indirect Adaptive Control of Systems with Unstable Discrete-Time Inverse
1 Introduction
2 Zeros of Discretized Systems
3 Overview of the Objective—Using a New Batch Stable Inverse to Address the Discrete Indirect Adaptive Control Inverse Problem
4 One Step Ahead Control, Batch Inverse, and Stable Batch Inverse
4.1 One Step Ahead Control
4.2 Batch Inverse Control
4.3 Stable Batch Inverse Control
5 Recursive Batch Stable Inverse Control
5.1 Creating a Model Predictive Control Model (MPC) from an ARX Model
5.2 Separating the Addressed Time Steps from the Unaddressed
5.3 The Recursive Update for Update j + 1
5.4 The Dynamics from Batch to Batch
6 Model Updates
6.1 ARX Model Updates
6.2 MPC Model Updates
7 Proposed Discrete Time Indirect Batch Inverse Adaptive Control
8 Comments and Discussion
8.1 Bound on the Order of the System
8.2 Bound on the Number of Zeros Outside the Unit Circle
8.3 On the Choice of Batch Size p to Produce a Stable Recursive Batch Update Process
8.4 Behavior at Unaddressed Time Steps
References
An Improved Conjugate Gradients Method for Quasi-linear Bayesian Inverse Problems, Tested on an Example from Hydrogeology
1 Introduction
2 Inverse Modelling in Hydrogeology
2.1 Bayesian Inversion
2.2 Maximum a Posteriori Estimation
2.3 Specialized Optimization Methods for MAP Estimates
3 Implementation Details
3.1 Program Structure
3.2 Parallelization
4 Numerical Results
4.1 Convergence Speed of Optimization
4.2 Accuracy of Inversion Results
4.3 Results for 3D Test Case
4.4 Parallel Efficiency
5 Summary and Conclusions
References
Idiosyncrasies of the Frequency Response of Discrete-Time Equivalents of Continuous-Time System
1 Introduction
2 Frequency Response of Differential Equations and Difference Equations
2.1 Frequency Response of Differential Equations
2.2 Converting a Differential Equation to a Difference Equation
2.3 Frequency Response of Difference Equations
3 Influence of a Zero Order Hold on Phase
4 The Zeros Introduced in Discretization
4.1 The Time Delay Going Through the Discrete Time System
4.2 The Asymptotic Zero Locations
4.3 Zero Locations as the Sample Time Interval Becomes Long
5 Phase Behavior at Fast Sample Rates
6 Phase Behavior as Nyquist Frequency is Reduced from High Frequency to First Singularity
7 Phase Behavior Crossing a Singularity
8 Phase Behavior for Nyquist Frequencies Below a Singularity
9 Summary
References