The Mathworks, Inc.

Vanderbilt University

Themathworks, Inc.

Pieter Johannes Mosterman

Jay Ryan Torgerson

Ebrahim Mehran Mestchian

Murali K Yeddanapudi

John Edward Ciolfi

Mojdeh Shakeri

Fu Zhang

Michael David Tocci

Zhi Han

Claudia G Wey

David H Koh

Robert Olson Aberg

Srinath Avadhanula

Andrew C Grace

Paul F Kinnucan

Justyna Zander

Ricardo Monteiro

Xiaocang Lin

Hidayet Tunc Simsek

Daniel Forrest Higgins

Farid Antoine Abizeid

Peter S Szpak

Murat Belge

Brian Keith Ogilvie

Nathan Edward Brewton

Haihua Feng

Tao Cheng

Ramamurthy Mani

Matthew J Englehart

Vijay Raghavan

Roy Lurie

Thomas Gaudette

Kiran K Kintali

Michael I Clune

Aravind Pillarisetti

Alan John Moore

Jesung Kim

Eric Pressman

Ryo Shimizu

Joseph R Bienkowski

Girish Venkataramani

Benjamin V Hinkle

Yao Ren

Michael H McLernon

Mehmet Yunt

Mary Ann Branch Freeman

Tunc Simsek

Atul Suri

Jean-Yves Brunel

Len Conte

Ravi Vompolu

Rohit Shenoy

Bella G Teverovsky

John Fluet

Craig Dowley

Ivan Garcia

Farid Antoine Abi-Zeid

Matthew Brian Behr

Gareth Thomas

Paul Cox

Xianchao Long

Gautam K Vallabha

E Mehran Mestchian

William James Aldrich

Joseph F Hicklin

Donald Paul Orofino, Ii

Vijaya Raghavan

Biao Yu

Jared D MacDonald

Andrew C Bartlett

Srikanth Parupati

Edward Whittington Gulley

Rajiv Ghosh-Roy

Zijad Galijasevic

Aditya Agrawal

Arwen M Warlock

Krishna Balasubramanian

Katalin Maria Popovici

Yit Phang Khoo

John P Dirner

Denizhan N Alparslan

Michael Patrick Bushe

Dongzhe Yang

Shomit Dutta

Ankit A Desai

Meera S Atreyam

Richard Hyde

Matthew J Simoneau

Ian Noell

Ashish Uthama

Simon Greenwold

Mark Reichelt

Suat Gumussoy

Leonard Conte

Ahmet Ozdemir

Jim Carrick

Alex Rodriguez

Rajesh Pavan Sunkari

Joe Conti

Eric J Manders

A method and system automatically generates a display of symbolic equations from a graphical model (or vice versa) which is readable, parametric, and interactive.

Systems and methods for interactive display of symbolic equations extracted from graphical models

A model including a first co-simulation component and a second co-simulation component is analyzed. During execution of the model, the first co-simulation component outputs data to the second co-simulation component via a connection. The connection is declared as a continuous-time rate connection for input of the data into the second co-simulation component. Based on analyzing the model, the connection is identified as a discrete-continuous sample time connection based on data being communicated from the first co-simulation component to the second co-simulation component via the connection at a discrete-time rate when the model is executed in a co-simulation manner.

Systems and methods for co-simulation

Model elements of an executable model, representing a physical system, are partitioned into one or more linear portions and one or more nonlinear portions. Simulating behavior of the physical system, by executing the model, includes, for each of multiple simulation time intervals, for a first nonlinear portion, computing a correlation matrix characterizing noise associated with one or more ports of the model. A scattering matrix corresponds to a portion of the physical system represented by the first nonlinear portion without accounting for any noise within the portion of the physical system. The correlation matrix is derived from the scattering matrix based on noise within the portion of the physical system. Noise sources representing noise within the portion of the physical system are identified based on the correlation matrix. At least one characteristic of noise associated with each noise source is computed, and noise characteristics are output at selected ports.

Estimating noise characteristics in physical system simulations

Systems and methods automatically construct a realization of a model from an available set of alternative co-simulation components, where the realization meets one or more objectives, such as fidelity, execution speed, or memory usage, among others. The systems and methods may construct the realization model by setting up and solving a constrained optimization problem, which may select particular ones of the alternative co-simulation components to meet the objectives. The systems and methods may configure the realization, and execute the realized model through co-simulation. The systems and methods may employ and manage different execution engines and/or different solvers to run the realization of the model.

Systems and methods for automatically realizing models for co-simulation

A method may comprise determining, by executing a first model having first configuration parameters, a first result associated with the first model. The method may comprise determining, by executing a second model having second configuration parameters, a second result associated with the second model. The method may comprise determining, based on the first result, the second result, and equivalency criteria, that the second model is not functionally equivalent to the first model. The equivalency criteria may indicate that the second model is functionally equivalent to the first model when a difference between the second result and the first result satisfies a threshold. The method may comprise modifying a configuration parameter, of the second configuration parameters, to cause the second model to improve toward functional equivalence with the first model.

Determining functional equivalence of configurations of a model

Systems and methods evaluate simulation models and measure floating point arithmetic errors in terms of Unit in Last Place (ULP). The simulation model may include model elements that perform numerical computations using Native Floating Point (NFP) arithmetic. The model elements may be arranged to implement a procedure. A data store may include local ULP errors predetermined for the model elements. The systems and methods may retrieve the local ULP errors for the model elements included in the model, and may apply a rules-based analysis to compute an overall ULP error of the simulation model. The systems and methods may present the overall ULP computed for the model. The systems and methods may also present intermediate ULP errors determined for portions of the simulation model. Changes may be made to the model to reduce the overall ULP error.

Systems and methods for measuring error in terms of unit in last place

A device generates a block for a model associated with a system, and the system is associated with middleware. The block subscribes to information generated by the middleware based on communication between the middleware and the system. The device receives subscriber configuration information for configuring the block, and creates, based on the subscriber configuration information, a signal that converts the information generated by the middleware into a format compatible with the model.

Exchanging information among system middleware and models

A system and method graphically display ports in a discrete event system (DES) environment. A graphical representation of a model having at least one DES component is provided in the DES environment. A first port of the DES component and a second port of the DES component are indicated by symbols. The first port is indicated by a first symbol representing a port type of the DES environment and the second port is indicated by a second symbol representing a port type of a non-DES environment.

Graphical representation of ports of multiple types

According to some possible implementations, a method may include determining one or more inputs to a model of a system and one or more outputs from the model. The method may include identifying a continuous portion of the model to be discretized. The method may include discretizing the continuous portion of the model, using at least one of a continuous linear representation for the model or a frequency response associated with the continuous linear representation, to generate a discrete linear representation for the continuous portion of the model. The method may include outputting information associated with the discrete linear representation to permit the continuous portion of the model to be implemented on one or more processors.

Discretization of a model of a system or a portion of the model using system inputs and outputs

In a graphical modeling environment supporting a model having at least two different analysis frameworks operating therein, a system and corresponding method of processing the graphical model modify the model to group model portions together for processing in the same analysis framework. Model parts are identified and associated with the analysis framework in which they operate. Model parts are then grouped based on their association with their analysis framework to form model portions that operate in one of the different analysis frameworks, In instances where topological separation of model portions operating in the same analysis framework occurs, the system and method reconfigure intervening model portions to be amenable with operation in the analysis framework of the surrounding model portions to improve processing efficiency.

System and method for graphical model processing

A device may determine temporal ages of a first signal and a second signal provided in a graphical model generated in a technical computing environment, where the first signal is different than the second signal. The device may determine whether the temporal age of the first signal is equivalent to the temporal age of the second signal at a particular block of the graphical model. The device may either display an indication that the first signal is synchronized with the second signal when the temporal age of the first signal is equivalent to the temporal age of the second signal, or may display another indication that the first signal is not synchronized with the second signal when the temporal age of the first signal is not equivalent to the temporal age of the second signal.

Identification and correction of temporal ages in separate signal paths of a graphical model

A device may receive a model that includes multiple blocks. The model may include first variables that contribute to a first calculation and second variables that contribute to a second calculation. The device may determine first dependencies associated with the first variables and may determine second dependencies associated with the second variables. The device may generate a first execution function based on determining the first dependencies. The first execution function may identify first blocks that are to be executed to perform the first calculation. The device may generate a second execution function based on determining the second dependencies. The second execution function may identify second blocks that are to be executed to perform the second calculation. The device may cause the first blocks and the second blocks to be executed in a different manner based on the first execution function and the second execution function.

Graph-based partitioning of dynamic system execution

Growing community of inventors

Framingham, MA, United States of America

Pieter Johannes Mosterman