umbridge

Help for package umbridge const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {umbridge} Contents apply_hessian apply_jacobian evaluate get_models gradient model_input_sizes model_output_sizes protocol_version_supported supports_apply_hessian supports_apply_jacobian supports_evaluate supports_gradient Title: Integration for the UM-Bridge Protocol Version: 1.0 Maintainer: Linus Seelinger <mail@linusseelinger.de> Description: A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc. License: MIT + file LICENSE Encoding: UTF-8 RoxygenNote: 7.2.1 BugReports: https://github.com/um-bridge Imports: httr2, jsonlite, magrittr Suggests: testthat (≥ 3.0.0) Config/testthat/edition: 3 NeedsCompilation: no Packaged: 2022-09-23 07:05:45 UTC; linus Author: Linus Seelinger [aut, cre] Repository: CRAN Date/Publication: 2022-09-23 07:30:02 UTC Evaluate Hessian of model. Description Evaluate Hessian of model. Usage apply_hessian( url, name, out_wrt, in_wrt1, in_wrt2, parameters, sens, vec, config = jsonlite::fromJSON("{}") ) Arguments url URL the model is running at. name Name of the desired model. out_wrt Output variable to take Hessian with respect to. in_wrt1 First input variable to take Hessian with respect to. in_wrt2 Second input variable to take Hessian with respect to. parameters Model input parameter (a list of vectors). sens Sensitivity with respect to output. vec Vector to multiply Hessian by. config Model-specific configuration options. Value Hessian with respect to given inputs and outputs, applied to given sensitivity and vector. Evaluate Jacobian of model. Description Evaluate Jacobian of model. Usage apply_jacobian( url, name, out_wrt, in_wrt, parameters, vec, config = jsonlite::fromJSON("{}") ) Arguments url URL the model is running at. name Name of the desired model. out_wrt Output variable to take Jacobian with respect to. in_wrt Input variable to take Jacobian with respect to. parameters Model input parameter (a list of vectors). vec Vector to multiply Jacobian by. config Model-specific configuration options. Value Jacobian with respect to given input and output variables, applied to given vector. Evaluate model. Description Evaluate model. Usage evaluate(url, name, parameters, config = jsonlite::fromJSON("{}")) Arguments url URL the model is running at. name Name of the desired model. parameters Model input parameter (a list of vectors). config Model-specific configuration options. Value The model output (a list of vectors). Get models supported by server. Description Get models supported by server. Usage get_models(url) Arguments url URL the model is running at. Value List of models supported by server. Evaluate gradient of target functional depending on model. Description Evaluate gradient of target functional depending on model. Usage gradient( url, name, out_wrt, in_wrt, parameters, sens, config = jsonlite::fromJSON("{}") ) Arguments url URL the model is running at. name Name of the desired model. out_wrt Output variable to take gradient with respect to. in_wrt Input variable to take gradient with respect to. parameters Model input parameter (a list of vectors). sens Sensitivity of target functional with respect to model output. config Model-specific configuration options. Value Gradient of target functional. Retrieve model's input dimensions. Description Retrieve model's input dimensions. Usage model_input_sizes(url, name, config = jsonlite::fromJSON("{}")) Arguments url URL the model is running at. name Name of the desired model. config Model-specific configuration options. Value List of input dimensions. Retrieve model's output dimensions. Description Retrieve model's output dimensions. Usage model_output_sizes(url, name, config = jsonlite::fromJSON("{}")) Arguments url URL the model is running at. name Name of the desired model config Model-specific configuration options. Value List of output dimensions. Check if model's protocol version is supported by this client. Description Check if model's protocol version is supported by this client. Usage protocol_version_supported(url) Arguments url URL the model is running at. Value TRUE if model's protocol version is supported by this client, FALSE otherwise. Check if model supports Hessian action. Description Check if model supports Hessian action. Usage supports_apply_hessian(url, name) Arguments url URL the model is running at. name Name of the desired model. Value TRUE if model supports Hessian action, FALSE otherwise. Check if model supports Jacobian action. Description Check if model supports Jacobian action. Usage supports_apply_jacobian(url, name) Arguments url URL the model is running at. name Name of the desired model. Value TRUE if model supports Jacobian action, FALSE otherwise. Check if model supports evaluation. Description Check if model supports evaluation. Usage supports_evaluate(url, name) Arguments url URL the model is running at. name Name of the desired model. Value TRUE if model supports evaluation, FALSE otherwise. Check if model supports gradient evaluation. Description Check if model supports gradient evaluation. Usage supports_gradient(url, name) Arguments url URL the model is running at. name Name of the desired model. Value TRUE if model supports gradient evaluation, FALSE otherwise.