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In this work, inspired in the symbolic dynamic of chaotic systems and using machine learning techniques, a control strategy for complex systems is designed. Unlike the usual methodologies based on modeling, where the control signal is…

Chaotic Dynamics · Physics 2021-06-08 Pedro García

The histogram is a key method for visualizing data and estimating the underlying probability distribution. Incorrect conclusions about the data result from over or under-binning. A new method based on the Shannon entropy of the histogram…

Data Analysis, Statistics and Probability · Physics 2022-10-07 Stephen Watts , Lisa Crow

The purpose of this article has two fold. The first is to generalize some recent second main theorems for the mappings and moving hyperplanes of $\P^n(\C)$ to the case where the counting functions are truncated multiplicity (by level $n$)…

Complex Variables · Mathematics 2019-02-13 Duc Thoan Pham , Hai Nam Nguyen , Van An Nguyen

Honeycomb-like microstructures have been shown to exhibit local elastic buckling under compression, with three possible geometric buckling modes, or pattern transformations. The individual pattern transformations, and consequently also…

Soft Condensed Matter · Physics 2020-04-14 O. Rokoš , M. M. Ameen , R. H. J. Peerlings , M. G. D. Geers

In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems. Specifically, we consider a situation where there are multiple sets of observations of dynamical systems…

Machine Learning · Computer Science 2017-11-28 Ershad Banijamali , Ahmad Khajenezhad , Ali Ghodsi , Mohammad Ghavamzadeh

Rigid bodies, plastic impact, persistent contact, Coulomb friction, and massless limbs are ubiquitous simplifications introduced to reduce the complexity of mechanics models despite the obvious physical inaccuracies that each incurs…

Robotics · Computer Science 2020-07-31 Aaron M. Johnson , Samuel A. Burden , Daniel E. Koditschek

We present SoftSMPL, a learning-based method to model realistic soft-tissue dynamics as a function of body shape and motion. Datasets to learn such task are scarce and expensive to generate, which makes training models prone to overfitting.…

Computer Vision and Pattern Recognition · Computer Science 2020-12-11 Igor Santesteban , Elena Garces , Miguel A. Otaduy , Dan Casas

Constructing reduced representations of high-dimensional systems is a fundamental problem in physical chemistry. Many unsupervised machine learning methods can automatically find such low-dimensional representations. However, an often…

Chemical Physics · Physics 2024-04-04 Jakub Rydzewski

Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and…

Robotics · Computer Science 2024-04-23 Kento Kawaharazuka , Shogo Makino , Masaya Kawamura , Yuki Asano , Kei Okada , Masayuki Inaba

We consider nonlinear multibody systems and present a suitable set of coordinates for the internal dynamics which allow to decouple the internal dynamics without the need to compute the Byrnes-Isidori form. Furthermore, we derive sufficient…

Dynamical Systems · Mathematics 2021-03-23 Lukas Lanza

This paper summarizes a research program to express the organization of sensorimotor control by specifying physiological states and the conditions for transitions among them. By a slight change in standard notation, conditional dynamics…

Biological Physics · Physics 2007-05-23 Gin McCollum

This article introduces a method for adjusting macro-particle weights within a particle distribution while preserving statistical and physical properties. The method allows the weights of the new macro-particle distribution to be determined…

Computational Physics · Physics 2024-03-19 Nicolas Pichoff , Samuel Marini

Simple models are preferred over complex models, but over-simplistic models could lead to erroneous interpretations. The classical approach is to start with a simple model, whose shortcomings are assessed in residual-based model…

Methodology · Statistics 2019-10-22 Torsten Hothorn

We distinguish a mechanical representation of the world in terms of point masses with positions and momenta and the chemical representation of the world in terms of populations of different individuals, each with intrinsic stochasticity,…

Statistical Mechanics · Physics 2019-05-07 Hong Qian

Importance sampling is a variance reduction technique for efficient estimation of rare-event probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested…

Probability · Mathematics 2007-05-23 Paul Dupuis , Hui Wang

We discuss general thermodynamic properties of molecular structure formation processes like protein folding by means of simplified, coarse-grained models. The conformational transitions accompanying these processes exhibit similarities to…

Soft Condensed Matter · Physics 2011-07-04 Michael Bachmann

Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower-dimensional manifold where we aim to infer subsets of…

Machine Learning · Statistics 2020-11-04 Lorena Romero-Medrano , Pablo Moreno-Muñoz , Antonio Artés-Rodríguez

The reduction of dynamical systems has a rich history, with many important applications related to stability, control and verification. Reduction of nonlinear systems is typically performed in an exact manner - as is the case with…

Optimization and Control · Mathematics 2007-07-26 Paulo Tabuada , Aaron D. Ames , Agung Julius , George J. Pappas

We consider tracking control for multibody systems which are modelled using holonomic and nonholonomic constraints. Furthermore, the systems may be underactuated and contain kinematic loops and are thus described by a set of…

Optimization and Control · Mathematics 2021-02-11 Thomas Berger , Svenja Drücker , Lukas Lanza , Timo Reis , Robert Seifried

We present a method which allows reduction of a size of a simulated system. The method can be applied to any system where one can define a finite set of possible states of the system and an elementary process which transforms one state of…

Computational Physics · Physics 2015-09-30 Malgorzata J. Krawczyk