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Data-free neural representation compression with riemannian neural dynamics 24

Data-free neural representation compression with riemannian ...

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Proceedings of the 41st International conference on Machine Learning

作者： Zhengqi Pei Anran Zhang Shuhui Wang Xiangyang Ji Qingming Huang Institute of Computing Technology Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences and Peng Cheng Laboratory Department of Automation Tsinghua University School of Computer Science and Technology University of Chinese Academy of Sciences and Institute of Computing Technology Chinese Academy of Sciences and Peng Cheng Laboratory

Neural models are equivalent to dynamic systems from a physics-inspired view, implying that computation on neural networks can be interpreted as the dynamical interactions between neurons. However, existing work models neuronal interaction as a weight-based linear transformation, and the nonlinearity comes from the nonlinear activation functions, which leads to limited nonlinearity and data-fitting ability of the whole neural model. Inspired by Riemannian geometry, we interpret neural structures by projecting neurons onto the Riemannian neuronal state space and model neuronal interaction with Riemannian metric (RieM), which provides a neural representation framework with higher parameter efficiency. With RieM, we further design a novel data-free neural compression mechanism that does not require additional fine-tuning with real data. Using backbones like ResNet and Vision Transformer, we conduct extensive experiments on datasets such as MNIST, CIFAR-100, ImageNet-1k, and COCO object detection. Empirical results show that, under equal compression rates and computational complexity, models compressed with RieM achieve superior inference accuracy compared to existing data-free compression methods.

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Applying Differentiable Programming to Kinetic Plasma Physics Simulations

Applying Differentiable Programming to Kinetic Plasma Physic...

引用

IEEE International conference on Plasma Science (ICOPS)

作者： A. Joglekar A. G. R. Thomas Ergodic LLC San Francisco California United States of America Center for Ultrafast Optical Sciences University of Michigan Ann Arbor Michigan United States of America

ISBN: (纸本)9781665479264

Plasma supports collective modes and particle-wave interactions that leads to complex behavior in fusion energy, and other applications. While plasma can sometimes be modeled as a charged fluids, the Vlasov-Poisson system of partial differential equations provides a description that is useful towards the study of nonlinear effects in the higher dimensional momentum-position phase-space that describes the full complexity of plasma dynamics. By constructing a Vlasov-Poisson solver using an auto-differentiable framework, we are able to perform gradient-based optimization of arbitrary functions of the simulation results with respect to input parameters. We validate the methodology and implementation by rediscovering resonances in the well-understood linear regime. Then, we use our solver to learn the parameters to a forcing function that reveal new non-linear effects in finite-length electrostatic plasma-wave propagation. We also discuss the effect of neural-reparameterization of the inputs on the optimization process.

关键词： Simulation Partial differential equations Fluid dynamics Programming Mathematical models Plasmas nonlinear dynamical systems

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Hardware Implementation of Low-complexity Deep Learning-based Model Predictive Controller

Hardware Implementation of Low-complexity Deep Learning-base...

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European Control conference (ECC)

作者： Mohanty, Nirlipta Ranjan Adhau, Saket Ingole, Deepak Sonawane, Dayaram Coll Engn Pune Shivajinagar 411005 India Norwegian Univ Sci & Technol Trondheim Norway Katholieke Univ Leuven Dept Mech Engn Leuven Belgium

ISBN: (纸本)9789463842365

Model predictive control (MPC) is an advanced control strategy that predicts the future behavior of the plant while considering the system dynamics and constraints. This optimization-based control algorithm needs huge amount of computational resources as it solves the optimization problem at each sampling time. This computational load demands powerful hardware and light-weight algorithms for implementing MPC on an embedded systems with limited computational resources. Deep neural network (DNN) is a attractive alternative for MPC as it provide the close approximation for various linear/nonlinear functions. In this paper, a feed forward neural network (FFNN) and recurrent neural network (RNN)-based linear MPC is developed. These neural network algorithms which are trained offline can efficiently approximate MPC control law which can be easily executed on low-level embedded hardware. The closed-loop performance is verified on a hardware-in-the-loop (HIL) co-simulation on ARM microcontroller. The performance of proposed DNN-MPC is demonstrated with a case study of a 2 degree-of-freedom (DOF) helicopter. Hardware result shows that the DNN-MPC is faster and consumes less memory as compared to MPC while retaining most of the performance indices.

关键词： Model predictive control deep neural network approximate control law linear systems HIL cosimulation

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Data-Driven Stability Verification of Homogeneous nonlinear Systems with Unknown dynamics*

Data-Driven Stability Verification of Homogeneous Nonlinear ...

引用

IEEE conference on Decision and Control

作者： Abolfazl Lavaei Peyman Mohajerin Esfahani Majid Zamani School of Computing Newcastle University United Kingdom Delft Center for Systems and Control Delft University of Technology The Netherlands Computer Science Department University of Colorado Boulder USA LMU Munich Germany

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this work, we propose a data-driven approach for the stability analysis of discrete-time homogeneous nonlinear systems with unknown models. The proposed framework is based on constructing Lyapunov functions via a set of data, collected from trajectories of unknown systems, while providing an a-priori guaranteed confidence on the stability of the system. In our data-driven setting, we first cast the original stability problem as a robust optimization program (ROP). Since unknown models appear in the constraint of the proposed ROP, we collect a finite number of data from trajectories of unknown systems and provide two variants of scenario optimization program (SOP) associated to the original ROP. We discuss that the proposed ROP, and its corresponding SOPs, are not convex due to having a bilinearity between decision variables. We also show that while one of the proposed SOPs is more efficient in terms of computational complexity, the other one provides Lyapunov functions with a much better performance for the original ROP. We then establish a probabilistic closeness between the optimal value of (non-convex) SOP and that of ROP, and subsequently, formally provide the stability guarantee for unknown systems with a guaranteed confidence level. We illustrate the efficacy of our proposed results by applying them to two physical case studies with unknown dynamics including (i) a DC motor and (ii) a (homogeneous) nonlinear jet engine compressor. We collect data from trajectories of unknown systems and verify their global asymptotic stability (GAS) with desirable confidence levels.

关键词： Asymptotic stability Probabilistic logic DC motors Stability analysis Data models Trajectory nonlinear systems

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KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in nonlinear Discrete-Time Systems

KCRL: Krasovskii-Constrained Reinforcement Learning with Gua...

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IEEE conference on Decision and Control

作者： Sahin Lale Yuanyuan Shi Guannan Qu Kamyar Azizzadenesheli Adam Wierman Anima Anandkumar Department of Computing and Mathematical Sciences Caltech Department of Electrical and Computer Engineering University of California San Diego Department of Electrical and Computer Engineering Carnegie Mellon University Nvidia Corporation

Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, the standard reinforcement learning (RL) methods lack formal stabilization guarantees, which limits their applicability for the control of real-world dynamical systems. We propose a novel policy optimization method that adopts Krasovskii's family of Lyapunov functions as a stability constraint. We show that solving this stability-constrained optimization problem using a primal-dual approach recovers a stabilizing policy for the underlying system even under modeling error. Combining this method with model learning, we propose a model-based RL framework with formal stability guarantees, Krasovskii-Constrained Reinforcement Learning (KCRL). We theoretically study KCRL with kernel-based feature representation in model learning and provide a sample complexity guarantee to learn a stabilizing controller for the underlying system. Further, we empirically demonstrate the effectiveness of KCRL in learning stabilizing policies in online voltage control of a distributed power system. We show that KCRL stabilizes the system under various real-world solar and electricity demand profiles, whereas standard RL methods often fail to stabilize.

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THERMAL MODELING USING TWO-PORT NETWORK IMPEDANCE FRACTIONAL-ORDER APPROXIMATIONS 17

THERMAL MODELING USING TWO-PORT NETWORK IMPEDANCE FRACTIONAL...

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ASME International Design Engineering Technical conferences / 41st Annual Computers and Information in Engineering conference (IDETC-CIE) / 17th IEEE/ASME International conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Duhe, Jean-Francois Victor, Stephane Melchior, Pierre Abdelmounen, Youssef Roubertie, Francois Univ Bordeaux Bordeaux INP Enseirb Matmeca CNRS IMS UMR 5218 351 Cours Liberat F-33405 Talence France Fdn Bordeaux Univ IHU Liryc Electrophysiol & Heart Modeling Inst F-33000 Bordeaux France INSERM Ctr Rech Cardiothorac Bordeaux F-33000 Bordeaux France

ISBN: (纸本)9780791885437

Sufficiently accurate thermal modeling is necessary for many applications such as heat dissipation, melting processes, building design or even bio-heat transfers in surgery. Circuit models help modeling heat transfer dynamics: this method is simple and is often used to model thermal phenomena. However, such models well approximates low and high frequency behavior but they are not accurate enough in the middle band of interest, thus lacking of precision in dynamical terms. A more complete and accurate description of conductive heat transfer can be obtained by using a two-port network. The resulting analytical expressions are complex and nonlinear in the frequency w. This complexity in the frequency domain is difficult to handle when it comes to control applications and more specifically in real-time applications such as surgery. Consequently, an analysis of this thermal two-port network in the frequency domain directly leads to fractional-order systems. A frequency domain analysis of the series and shunt impedances will be presented and different approximations will be explored in order to obtain simple but sufficiently precise linear fractional transfer function models. The series impedances are approximated by using asymptotic and pole-zero approximations and the shunt impedance is approximated by using a capacitance approximation and two fractional model approximations.

关键词： Modeling Approximation Surgery Heat Heat transfer Capacitance Structural design Transfer functions Poles (Building) Energy dissipation Melting Circuits Frequency-domain analysis dynamics (Mechanics)

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Optimal Control for Continuous-Time Scalar nonlinear Systems With Known dynamics 16

Optimal Control for Continuous-Time Scalar Nonlinear Systems...

引用

16th IEEE International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Tymoshchuk, Pavlo Silesian Tech Univ Dept Elect Engn & Comp Sci Gliwice Poland Lviv Polytech Natl Univ Dept Comp Aided Design Lvov Ukraine

ISBN: (纸本)9781728177090

An optimal controller for affine in the inputs scalar continuous-time nonlinear systems with the known dynamics is presented. The controller is described by an algebraic equation with variable structure. The control is derived in analytical way. A functional block-diagram of the controller is presented. The controller does not require any training procedure and is of moderate complexity. It is shown that the state-variable trajectories of the controlled system are globally asymptotically stable and globally asymptotically convergent to steady states. Computer simulations confirming theoretical derivations and illustrating the controller performance are provided.

关键词： optimal control nonlinear system functional block-diagram steady-state stability analysis convergence

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Application of sparse identification of nonlinear dynamics for physics-informed learning

Application of sparse identification of nonlinear dynamics f...

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IEEE Aerospace conference

作者： Corbetta, Matteo NASA Ames Res Ctr SGT Inc Moffett Field CA 94035 USA

ISBN: (纸本)9781728127347

Advances in machine learning and deep neural networks have enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions.

关键词： Deep neural networks

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Distributed Control of Networked Systems with nonlinear dynamics and Coupling Constraints

Distributed Control of Networked Systems with Nonlinear Dyna...

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European Control conference (ECC)

作者： Zonglin Liu Olaf Stursberg Control and System Theory EECS University of Kassel Germany

This paper introduces a distributed control strategy for constraint-coupled networked systems with nonlinear dynamics. Problems of this type are often too complicated to be solved in a centralized way, such that efficient distributed solution is required. The proposed method first casts the global problem into a mixed-integer programming (MIP) problem, and then solves the MIP problem by use of recently developed distributed solution schemes for this problem class. A reduction of the computational complexity compared to the centralized solution as well as the satisfaction of all constraints are guaranteed, and its efficiency is illustrated for a numeric example.

关键词： Couplings Decentralized control Europe Programming nonlinear dynamical systems Trajectory Computational complexity

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Overcoming the challenge of complexity: a new data analytics framework for power and water demand forecasting

Overcoming the challenge of complexity: a new data analytics...

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Saudi Arabia Smart Grid conference (SASG)

作者： Arndt Telschow Artemy Voroshilov Rene Böringer Cuculus GmbH D-98693 Ilmenau Germany

A major challenge in managing critical infrastructure such as power grids and water supply systems is to continually balance generation and demand. A reliable forecast is essential to optimize production and maintain grid stability. However, dynamics of power generation and consumption in modern grids are becoming increasingly difficult to predict due to dependencies on meteorological and socio-economic factors. Here we present a new data analysis framework designed to overcome such complexity. The forecast itself is generated using nonlinear time series analysis combined with machine learning. However, to reduce complexity, the forecast is made either by strictly univariate analysis or after filtering by causal interference analysis. The method thus provides good forecasts even for complex, high-dimensional situations in which classic methods usually fail. We illustrate the performance of the method using real data from the most important use cases of load and renewable energy forecasting (see https://***/ for a live demo).

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