Patent Publication Number: US-2021173011-A1

Title: Physics-Based Artificial Intelligence Integrated Simulation and Measurement Platform

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/945,008, titled “Physics-Based Artificial Intelligence Integrated Simulation and Measurement Platform,” filed by Hamed Kajbaf, on 6 Dec. 2019. 
     This application incorporates the entire contents of the above-referenced application herein by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to integrated simulation and modeling based on artificial intelligence (AI). 
     BACKGROUND 
     Electronics is a field related to electrons. Electronic devices interact with electrons. An electronic device may influence an electron&#39;s behavior, and produce a technical effect. Some electronic devices manipulate electron behavior. For example, an amplifier may increase a signal&#39;s energy. Some electronic devices react to electron behavior. In an illustrative example, an attenuator may reduce a signal&#39;s energy. Various electronic devices may be active, passive, used alone, or configured in combination for use with other electronic devices. 
     Users of electronic devices include individuals, computer applications, organizations, government, and industry. Users may employ electronic devices to perform tasks the user may not otherwise accomplish without the electronic device. For example, a human mobile electronic device user may be able to join in a videoconference linking participants dispersed throughout the world. This facilitation may involve many complex devices interoperating through a network. Each electronic device in such a network of interoperating devices may include component, sub-system, or system level elements. For example, a single mobile communication device may include multiple computation, communication, and interface elements, configured together to perform the functions of the mobile communication device. In an illustrative example, each of the mobile communication device&#39;s component elements must operate together as designed, if the device is to function as intended. 
     Electronic device operating parameters include voltage, current, frequency, electromagnetic field strength, and other physical quantities. Device operating parameters may be determined based on device physics, the configuration of the device in a system, the device input, or other factors. A device behavioral model may predict device operating parameters. For example, a behavioral model may be designed to predict an electronic circuit&#39;s output determined as a function of an arbitrary input. In an illustrative example, predicting the operation of a complex device may require simulating models at component, sub-system, and system levels, to determine if the device might operate as designed before the device is physically constructed. A designer may expend significant time and effort simulating the operation of a system including many complex devices, each simulated based on multiple component, sub-system, and system models. 
     SUMMARY 
     Apparatus and associated methods relate to augmenting a device model identified by artificial intelligence, with measurements of physical parameters, iteratively validating and verifying the augmented model until the augmented model satisfies a quality criterion determined as a function of the artificial intelligence, and automatically synthesizing an interactive simulation and measurement environment, based on the model. The model may be identified by the artificial intelligence based on measurement of a device operating characteristic. The physical parameter measurements the model is augmented with may be determined by the artificial intelligence, based on the model. The model may include a component, sub-system, and system model, permitting validation and verification through multiple levels. Various implementations may automatically generate a measurement scenario including communication commands configured to validate and verify the augmented model. Some designs may provide visualization of synthesized simulation and measurement output generated as a function of the validated and verified augmented model. 
     In an aspect, an apparatus is disclosed, comprising: a processor; and memory that is not a transitory propagating signal, said memory comprising instructions and data, and said memory further configured to be operably coupled to the processor, wherein the memory comprises encoded data and processor-executable program instructions, wherein the data and the instructions jointly configure and program the apparatus such that, when executed by the processor, the data and the instructions cause the apparatus to perform operations comprising: identifying the type of a device based on measuring a device operating characteristic; select a device behavioral model based on the identified device type, wherein the model is configured to model a parameter of the device; augment the model with a physical measurement of the modeled parameter identified as a function of the selected model; iteratively validate and verify the modeled parameter and the measured parameter, until an evaluation of the modeled parameter and the measured parameter satisfies a quality criterion determined as a function of an artificial intelligence; and provide access to the validated and verified model augmented with the measured physical parameter, for generating synthesized simulation and measurement output, based on the model. 
     The model may further comprise a physics-based model. 
     The operations performed by the apparatus may further comprise train the artificial intelligence with a physical model based on simulated data. 
     The model may further comprise a component model. 
     The model may further comprise a sub-system model. 
     The model may further comprise a system model. 
     Validate the modeled parameter and the measured parameter may further comprise determine if the physical parameter modeled is correct, based on measurement. 
     Verify the modeled parameter and the measured parameter may further comprise the measured parameter evaluated as a function of another verified measurement. 
     The quality criterion may be a threshold predetermined as a function of the modeled parameter. 
     The quality criterion may be a threshold predetermined as a function of the measured parameter. 
     The quality criterion may be a statistical function of the measured parameter. 
     The quality criterion may be a statistical function of the modeled parameter. 
     The quality criterion may be a function of the measured parameter evaluated in the frequency domain. 
     The quality criterion may be a function of the modeled parameter evaluated in the frequency domain. 
     The quality criterion may be a function of the measured parameter evaluated in the time domain. 
     The quality criterion may be a function of the modeled parameter evaluated in the time domain. 
     The quality criterion may be a function of bandwidth. 
     The quality criterion may be a function of frequency selectivity. 
     The quality criterion may be a function of sensitivity. 
     Provide access to the validated and verified model augmented with the measured physical parameter may further comprise the access provided to a graphical user interface configured to visualize the synthesized simulation and measurement output. 
     In another aspect, an apparatus is disclosed, comprising: a processor; and memory that is not a transitory propagating signal, said memory comprising instructions and data, and said memory further configured to be operably coupled to the processor, wherein the memory comprises encoded data and processor-executable program instructions, wherein the data and the instructions jointly configure and program the apparatus such that, when executed by the processor, the data and the instructions cause the apparatus to perform operations comprising: train an artificial intelligence with a physical model based on simulated data; identifying the type of a device under test (DUT), based on a measured device operating characteristic evaluated by the artificial intelligence; select a physics-based device behavioral model based on the identified device type, wherein the model is configured to predict a plurality of device parameters; augment the model with physical measurements of the modeled parameters, wherein the parameters the model is augmented with are identified by the artificial intelligence as a function of the selected model; iteratively validate and verify the modeled parameters and the measured parameters, until an evaluation of the modeled parameters and the measured parameters satisfies a quality criterion determined as a function of the artificial intelligence; and provide access in a graphical user interface to the validated and verified model augmented with the measured physical parameters, for generating a visualization of synthesized simulation and measurement output, based on the model. 
     The physics-based device behavioral model may further comprise: a component model; a sub-system model determined as a function of the component model; a system model determined as a function of the sub-system model; and a measurement model determined as a function of measurement setup. 
     Iteratively validate and verify the modeled parameters and the measured parameters may further comprise validate and verify based on model levels comprising: measurement, component, sub-system, and system, until the criterion is satisfied for all levels. 
     The measured parameter may be selected from the group consisting of current, electromagnetic field strength, frequency, impedance, and voltage. 
     The device under test may further comprise an amplifier. 
     The device under test may further comprise an attenuator. 
     The measured device operating characteristic may further comprise two-port insertion loss. 
     The measured device operating characteristic may further comprise two-port insertion gain. 
     In another aspect, an apparatus is disclosed, comprising: a processor; and memory that is not a transitory propagating signal, said memory comprising instructions and data, and said memory further configured to be operably coupled to the processor, wherein the memory comprises encoded data and processor-executable program instructions, wherein the data and the instructions jointly configure and program the apparatus such that, when executed by the processor, the data and the instructions cause the apparatus to perform operations comprising: train an artificial intelligence with a physical model based on simulated data; identifying the type of a device under test, based on measured device circuit network parameter evaluated by the artificial intelligence; select a physics-based device behavioral model based on the identified device type, wherein the model is configured to predict a plurality of device parameters, and wherein the model comprises: a component model; a sub-system model determined as a function of the component model; and a system model determined as a function of the sub-system model; augment the model with physical measurements of the modeled parameters, wherein the parameters the model is augmented with are identified by the artificial intelligence as a function of the selected model, and wherein the measured parameter is selected from the group consisting of current, electromagnetic field strength, frequency, impedance, and voltage; iteratively validate and verify the modeled parameters and the measured parameters based on a measurement scenario automatically prepared by the trained artificial intelligence, until an evaluation of the modeled parameters and the measured parameters for the component, sub-system, and system models satisfies a quality criterion determined as a function of the artificial intelligence; and provide access in a graphical user interface to the validated and verified model augmented with the measured physical parameters, for generating a visualization of synthesized simulation and measurement output, based on the model. 
     The operations performed by the apparatus may further comprise in response to determining a discrepancy between measured and modeled parameters, automatically adjust the measurement scenario. 
     The measurement scenario may further comprise commands configured to communicate with measurement instruments. 
     The artificial intelligence may be selected from the group consisting of a machine learning algorithm, an artificial neural network, and principle component analysis. 
     In another aspect, an apparatus is disclosed, comprising: a processor; and memory that is not a transitory propagating signal, said memory comprising instructions and data, and said memory further configured to be operably coupled to the processor, wherein the memory comprises encoded data and processor-executable program instructions, wherein the data and the instructions jointly configure and program the apparatus such that, when executed by the processor, the data and the instructions cause the apparatus to perform operations comprising: train an artificial intelligence with a physics-based behavioral model of a circuit based on simulated data generated based on the circuit model; identifying the type of a device under test as a circuit, based on measured circuit s-parameters evaluated as a function of frequency by the trained artificial intelligence; select a physics-based system-level behavioral model configured to predict a plurality of device, sub-system, and system parameters, wherein the model comprises: a circuit model; a sub-system model determined as a function of the circuit model; and a system model determined as a function of the sub-system model; augment the system-level model with physical measurements of the modeled parameters, wherein the parameters the system-level model is augmented with are identified by the artificial intelligence as a function of the circuit model, and wherein the measured parameter is selected from the group consisting of current, electromagnetic field strength, frequency, impedance, and voltage; iteratively validate and verify the modeled parameters and the measured parameters based on a measurement scenario automatically prepared by the trained artificial intelligence, until an evaluation of the modeled parameters and the measured parameters for the component, sub-system, and system models satisfies a quality criterion determined as a function of the artificial intelligence; and provide access in a graphical user interface to the validated and verified model augmented with the measured physical parameters, for generating a visualization of synthesized simulation and measurement output, based on the model. 
     The modeled parameters may further comprise an s-parameter. 
     The measured parameters may further comprise an s-parameter. 
     Augment the system-level model may further comprise link a physical measurement with a modeled parameter. 
     Provide access in the graphical user interface to the validated and verified model may further comprise a physical measurement correlated with a modeled parameter in a virtual environment. 
     The measurement scenario may further comprise commands configured to communicate with measurement instruments. 
     The measurement scenario may further comprise physical parameters measured by a vector network analyzer. 
     The operations performed by the apparatus may further comprise exchange a measured parameter with a simulation tool. 
     The operations performed by the apparatus may further comprise exchange a modeled parameter with a simulation tool. 
     The operations performed by the apparatus may further comprise exchange data with a Computer Aided Design (CAD) environment or a Printed Circuit Board (PCB) layout environment, to overlap with measurement or simulation data. 
     The present disclosure teaches a Simulation and Measurement System or Method. The Simulation and Measurement System may be a computer-implemented Simulation and Measurement Platform. The Simulation and Measurement Method may be a process implementing Simulation and Measurement Platform features. The computer-implemented Simulation and Measurement Platform may include computer hardware. The computer-implemented Simulation and Measurement Platform may include computer software. The computer hardware may include a processor and a memory. The memory may encode processor executable program instructions configured to cause the hardware to perform the disclosed Simulation and Measurement operations. The computer hardware may include interfaces designed to permit the processor to interact with and capture measurements from a device under test using various test and measurement instruments. 
     An exemplary Simulation and Measurement Platform implementation of a software and hardware integrated platform for a physics-based artificial intelligence (AI) configured to combine measurement and simulation may permit a user to acquire data from measurement instruments, process the data, and synthesize the measured and simulated data in a single environment. The disclosed software includes implementation of the process using computer codes, including processor executable program instructions, which may be executed on a personal computer, server, digital signal processor, cloud computing, or other computational hardware platform as may be available to one of ordinary skill. The hardware may include an interface configured to interact with or control a measurement instrument, an electrical or electronics component, the device under test, or any other physical component used in the measurement process. The measurement instrument may be a hardware apparatus configured to acquire a physical quantity and convert the physical quantity to a digital or analog signal which can communicate with the software platform. Some examples of the measurement instruments include sensors, data acquisition cards (DAQ), spectrum analyzers, oscilloscopes, vector network analyzers (VNA), time domain reflectometers (TDR), signal analyzers, and the like, as would be known to one of ordinary skill. 
     An exemplary Simulation and Measurement Platform may be configured to provide an integrated software and hardware environment facilitating the data measurement, simulation, visualization, correlation, and management for these design stages, with a cohesive integrated software and hardware platform capable of communicating and interacting with various devices and instruments, including, for example: measurement instruments; control electronic boards (for example, data acquisition (DAQ), analog to digital converter (ADC)/digital to analog converter (DAC), single-board computer, programmable logic controller (PLC), relay, and the like); control motorized moving structures or robotic arms; and, exchange data with simulation tools, computer-aided design (CAD), and printed circuit board (PCB) layout tools. 
     An exemplary Simulation and Measurement Platform software implementation may include a physics-based environment configured to perform operations such as: acquiring physical quantities based on pre-defined procedures/standards or customized procedures; self-correlating or cross-correlating various physical quantities; 0D, 1D, 2D, or 3D spatial measurement in time, frequency, or time-frequency domain; exchange data with simulation tools; post-process acquired data from measurement or simulation environments; manage large quantities of data across multiple users and over a communication network; simulate a virtual electromagnetic or circuit equivalent environment; exchange data with CAD and PCB layout environments to overlap with measurement or simulation data; provide access to a library of measurement components configured with a behavioral model designed to simulate component characteristics; and, provide an intuitive visualization and reporting environment. 
     An exemplary Simulation and Measurement Platform software design may be configured to facilitate an iterative validation and verification process, to provide an integrated measurement and simulation environment. The software may be configured to communicate with measurement instruments, and standard machine and/or human readable data exchange formats, such as Touchstone (SnP), IEC TR 91967-1-1, Measurement Data Format (MDF), and other formats as may be available to one of ordinary skill. The software may be configured to verify imported data in the imported environment for accuracy and compatibility with physical quantities. In a more advanced scenario, the software may be configured to permit automatically or semi-automatically adjusting simulation setup or measurement instrument settings, if discrepancies are observed. For example, the software may be configured to change meshing criteria accordingly, if the simulation tool detects a specific or predetermined pattern from measured near-field scanned data. In another example scenario, if a measurement instrument determines based on simulation results that the expected signal level is smaller than the current noise level, the measurement bandwidth or dynamic range may be adjusted accordingly. An exemplary Simulation and Measurement Platform software implementation may be configured in a modular design, to have the capability to define a measurement setup based on user-defined modules. In an illustrative example, such user-defined modules may be assembled together as templates for measurement standards, or common practices. The AI may also be implemented as library of AIs, and may be controlled by a higher level AI. 
     An exemplary Simulation and Measurement Platform simulation design may include implementation of a physical model in the disclosed software platform, or in a third-party software platform which can exchange data with the software platform. The physics-based feature of the software may be implemented by software configured to process the data based on the electromagnetics, physical interpretation of the quantity, the physical dimensions (units), or mathematical models representing the physics governing the device or system under test. In an illustrative example, the software may be configured to use artificial intelligence models to create an augmented physical environment (APE) through the graphical user interface (GUI) for easy correlation of physical measured data with simulated data in a virtual environment in the software platform. The virtual environment includes an implementation in the software platform representing the physical model of the hardware platform. The augmented physical environment includes linking and correlation between components of the physical quantity or the physical device under test (DUT) to the physics-based model in the virtual environment. This integration is designed to perform the data-to-information conversion even for a user without advanced training. The data may include the raw numerical data acquired by a measurement instrument. The information includes presentation of the data in a format that is easy to interpret for a human user, such as, for example, plots, diagrams, flow-charts, and the like. The disclosed software implementation facilitates training of an artificial intelligence with simulated data generated by a physics-based model. The artificial intelligence may be implemented as a machine learning (ML) algorithm. The artificial intelligence may be configured with optimizations such as, for example, artificial neural networks (ANN), embedded mapping, or principle component analysis (PCA). The trained AI may then be used to automatically prepare a measurement scenario. The measurement scenario may include a set of communication commands (for example, SCIPI) configured to communicate with measurement instruments. 
     An exemplary Simulation and Measurement Platform may perform systematic synthesis of measurement and simulation based on iterative validation and verification. Validation may include the software performing operations including measurement and simulation to answer the question “Is the software measuring and/or modeling the right physical quantity?” to assure the design of each measurement or simulation process and the choice of metric meets the final needs, in manufacturing, test, and/or operational conditions. Verification may include the software performing operations including measurement and simulation to answer the question “Is the software measuring and/or modeling the physical quantity correctly?” based on evaluating the measurement process with another verified measurement or simulation, and evaluating the simulation design process by another verified simulation or measurement with shared governing physics and shared evaluation metrics. 
     In an illustrative example, the disclosed iterative validation and verification process may be based on evaluation of each contributing component to a system level measurement and simulation. The software may then generate (or acquire from a third-party software) a physics-based behavioral model which represents the relevant electrical or electromagnetic behavior of the components or sub-systems under test and the relationship between components and the system level evaluation. Once individual components of the simulation and measurement processes are verified, the components can be used as a tool for understanding the system behavior which are otherwise difficult to characterize. 
     Various Simulation and Measurement Platforms may achieve one or more technical effect. For example, some Simulation and Measurement Platforms may improve a user&#39;s ease of access to system simulation. This facilitation may be a result of reducing the user&#39;s effort adjusting a device under test model and configuring the device model with a system model in the user&#39;s simulation and measurement setup. In some Simulation and Measurement Platforms, a device under test model and the device parameters to be simulated may be automatically selected for a user based on a device physical measurement evaluated by artificial intelligence. For example, the artificial intelligence may be trained with simulated data generated from a physics-based device model, and the trained artificial intelligence may identify a device model based on matching measurement of a physical device under test with the simulated data. Such automatic device under test and simulation parameter identification may reduce a user&#39;s exposure to the risk of error in model selection, and improve the user&#39;s confidence that a simulated model type matches the device under test. 
     Some Simulation and Measurement Platforms may reduce a user&#39;s effort obtaining verified measurement results related to a user&#39;s testing or development of an electronic system design. Such reduced effort obtaining verified measurement results may be a result of an iterative validation and verification process evaluating each contributing component to a system level measurement and simulation. Such verified measurement results may improve the user&#39;s simulation, modeling, and measurement experience. For example, verified individual components of the simulation and measurement processes may aid evaluation of complex system behavior, permitting a user to adjust system designs more quickly, and improve the accuracy or usefulness of research and development testing. In some Simulation and Measurement Platforms, a user&#39;s understanding of system behavior may be improved. Such improved understanding of system behavior may be a result of providing the user access to a visual Augmented Physical Environment (APE) correlating device under test physical measurement with simulated data in a virtual lab. For example, a user may more quickly understand the effect of a design change, based on augmented simulation visualized by the APE linking and correlating device under test physical measurements with data generated by a physics-based model in a measurement scenario generated by artificial intelligence. 
     In an illustrative example, design and production of an electronic board may require multiple iterations of simulation, measurement, validation, and verification, as well as pre-compliance and compliance tests. An exemplary implementation of a software and hardware integrated platform for a physics-based artificial intelligence (AI) configured to combine measurement and simulation is herein disclosed. The exemplary software implementation creates a cohesive and integrated platform designed to combine measurement and simulation, based on artificial intelligence models. 
     A “working example” of one aspect/embodiment of the instant invention works as follows for, inter alia, Cable Impedance Measurement and Simulation: 
     In one such example, the instant invention utilizes its “V-model” ( FIG. 5 ) to iteratively use syntheses of measurement and simulation to extract the model of an HDMI cable over [a] ground plane. A cable assembly is placed on top of a metallic ground plane. The cable assembly on top of the ground plane is considered a “system” based on the V-model. Cable assembly is the “sub-system.” The metallic ground plane, the cable, and the ferrite are the “components” of the setup.
 
Here, the “system” would be the device being tested, here an HDMI cable over the ground plane, e.g. a wire with diameter 4 mm, substrate height 32 mm, and substrate dielectric Er 1.1; Output impedance 206 Ohms.
 
Here, the “sub system” is the cable assembly over the ground plane.
 
Here, the “component” would be, e.g., ferrite.
 
Here, another “component” would be the ground plane.
 
This example of the invention at work would have an initial Measurement Setup comprising a measuring instrument, here a vector network analyzer, and the device tested would be the HDMI cable over ground plane.
 
The invention then performs sub-system modeling of the cable over ground plane using third-party impedance calculator.
 
The simulation modeling of the system, sub-system, and components are performed in a third-party circuit simulator.
 
The model of the transfer impedance of the cable is extracted from the impedance calculator, which could be, for example, 206 Ohms with, for example, a time delay of 0.4 nano-seconds.
 
The invention then performs system, sub-system, and component level modeling using said third-party software tool.
 
The model of ferrite and the parameters of the model are adjusted iteratively using syntheses of measurement and simulation per the invention&#39;s apparatus and method.
 
Results: Measurement results are “verified” in the measurement setup with or without the ferrite on the cable assembly. Namely, the invention is therein able to compare measurements of DUT with ferrite and without ferrite.
 
The invention is then able to measure return loss of the cable using a vector network analyzer. Return loss can then be measured showing the various margins (e.g. between 10 and 60 dBΩ) at various frequencies (between, e.g. 1 MHz and 1 GHz).
 
Based on the above synthesis of the measurement and simulation, the model parameters of the ferrite on the cable assembly would be, for example, extracted as follows:
 
     R Fer =89Ω 
     L Fer =293 nH 
     C Fer =0.2 pF 
     In this example, the above parameters are then visually modeled and displayed via Graphical User Interface appropriately configured to visually illustrate the above parameters in their assiciated units and ratios. 
     The details of various aspects are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts an illustrative operational scenario synthesizing an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. 
         FIG. 2  depicts a schematic view of an exemplary simulation and measurement network configured with an Artificial Intelligence Integrated Simulation and Measurement Platform (AIISMP) programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. 
         FIG. 3  depicts a structural view of an exemplary AIISMP configured with an Augmented Physical Measurement and Simulation Environment Engine (APMSEE) programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. 
         FIG. 4  depicts an exemplary integrated simulation and measurement software architecture. 
         FIG. 5  depicts an exemplary measurement and simulation synthesis process model. 
         FIG. 6  depicts an exemplary integrated simulation and measurement information flow. 
         FIG. 7  depicts a schematic view of an exemplary integrated simulation and measurement setup. 
         FIG. 8  depicts a process flow of an exemplary APMSEE programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented based on artificial intelligence. 
         FIG. 9  depicts an exemplary simulation and measurement configuration. 
     
    
    
     Like reference symbols in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
     To aid understanding, this document is organized as follows. First, synthesizing an integrated simulation and measurement environment is briefly introduced with reference to  FIG. 1 . Second, with reference to  FIGS. 2-3 , the discussion turns to exemplary implementations that illustrate integrated simulation and measurement system design. Specifically, integrated simulation and measurement network and platform implementations are disclosed. Finally, with reference to  FIGS. 4-9 , various aspects of an exemplary Simulation and Measurement Platform design are presented to explain improvements to integrated simulation and measurement technology. 
       FIG. 1  depicts an illustrative operational scenario synthesizing an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. In  FIG. 1 , the user  105  evaluates the device under test (DUT)  110  via the network cloud  115  operably coupling the user device  120  with the Artificial Intelligence Integrated Simulation and Measurement Platform (AIISMP)  125 . In the depicted implementation, the AIISMP  125  is configured to iteratively validate and verify a physics-based model of the DUT  110 , until an evaluation of the modeled parameters and the measured parameters satisfies a quality criterion. The quality criterion may be determined as a function of an artificial intelligence trained with simulated data generated by a DUT  110  behavioral model. In the depicted implementation, the model database server  130  is operably coupled with the network cloud  115  to retrievably store behavioral models accessible to the AIISMP  125 . In the illustrated implementation, the AIISMP  125  is operably coupled with the DUT  110  via the measurement instrument  135 . In the illustrated implementation, the measurement instrument  135  is operably coupled with the AIISMP  125  to facilitate measurement and control of the DUT  110 . 
     In an illustrative example, the AIISMP  125  may retrieve the DUT  110  model  140  from the model database server  130 . The model  140  may be a behavioral model configured to predict a DUT  110  physical operating parameter. The behavioral model  140  may be a physics-based model of a component, sub-system, or system. The AIISMP  125  may augment the model  140  with measurement  145  of parameter  150  to create the synthesized integrated simulation and measurement environment  155 . The AIISMP  125  may provide the synthesized integrated simulation and measurement environment  155  to the user  105  via the user device  120  user interface. The synthesized integrated simulation and measurement environment  155  may be referred to as an APE (Augmented Physical Environment). 
     In the depicted implementation, the AIISMP  125  generation of the DUT  110  APE begins at step  160  with the AIISMP  125  selecting the physics-based DUT  110  model  140  and physical parameters  150  to be validated and verified. The model  140  may include setup, component, sub-system, and system model levels. At step  165 , the AIISMP  125  captures physical measurement  145  from the DUT  110  using the measurement instrument  135 . The AIISMP  125  compares the measurement  145  to the model  140  prediction of the parameter  150 . At step  170 , the AIISMP  125  validates the selected parameter  150 , based on the comparison. At step  175 , the AIISMP  125  verifies the modeled parameter  150  based on the measurement  145 . At step  180 , the AIISMP  125  performs a test to determine if the parameter  150  has been validated and verified with the measurement  145 . Upon a determination by the AIISMP  125  at step  180  the parameter  150  has not been validated and verified, the AIISMP  125  continues at step  190  augmenting the model  140  with the measurement  145 , and the AIISMP  125  operation continues at step  165 . Upon a determination by the AIISMP  125  at step  180  the parameter  150  has been validated and verified, the AIISMP  125  at step  185  performs a test to determine if all parameters have been validated and verified for all model  140  levels. Upon a determination by the AIISMP  125  at step  185  all parameters  150  have not been validated and verified for all model  140  levels, the AIISMP  125  continues at step  190  augmenting the model  140  with the measurement  145 . Upon a determination by the AIISMP  125  at step  185  all parameters  150  have been validated and verified for all model  140  levels, the AIISMP  125  at step  195  provides the validated and verified augmented physical environment  155  to the user device  120 . The process may repeat. 
       FIG. 2  depicts a schematic view of an exemplary simulation and measurement network configured with an Artificial Intelligence Integrated Simulation and Measurement Platform (AIISMP) programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. In  FIG. 2 , according to an exemplary implementation of the present disclosure, data may be transferred to the system, stored by the system and/or transferred by the system to users of the system across local area networks (LANs) or wide area networks (WANs). The system may include numerous servers, data mining hardware, computing devices, or any combination thereof, communicatively connected across one or more LANs and/or WANs. One of ordinary skill in the art would appreciate that there are numerous manners in which the system could be configured, and implementations of the present disclosure are contemplated for use with any configuration. Referring to  FIG. 2 , a schematic overview of a system implementation in accordance with the present disclosure is shown. In the depicted implementation, the exemplary system includes the exemplary user device  120  configured to provide user access to an Augmented Physical Environment. In the illustrated implementation, the AIISMP  125  is a computing device configured to generate the Augmented Physical Environment based on synthesizing a simulation and measurement environment for the DUT  110 . In the illustrated implementation, the DUT  110  is an electronic device subjected to synthesized simulation and measurement in the Augmented Physical Environment. The DUT  110  may be an amplifier. The DUT  110  may be an attenuator. The DUT  110  may be an equalizer. The DUT  110  may be a filter. The DUT  110  may be a coupler. In the depicted implementation, the model database server  130  is a cloud storage database configured to retrievably store behavioral models. In the illustrated implementation, the measurement instrument  135  is a measurement instrument configured to capture physical parameter measurements from the DUT  110 , under the control of the AIISMP  125 . In the illustrated implementation, the user device  120  is communicatively and operably coupled by the wireless access point  201  and the wireless link  202  with the network cloud  115  (for example, the Internet) to send, retrieve, or manipulate information in storage devices, servers, and network components, and exchange information with various other systems and devices via the network cloud  115 . In the depicted implementation, the illustrative system includes the router  203  configured to couple the AIISMP  125  communicatively and operably to the network cloud  115  via the communication link  204 . In the illustrated implementation, the router  203  also communicatively and operably couples the model database server  130  to the network cloud  115  via the communication link  205 . In the depicted implementation, the measurement instrument  135  is communicatively and operably coupled with the network cloud  115  by the wireless access point  206  and the wireless communication link  207 . In the illustrated implementation, the DUT  110  is operably coupled with the AIISMP  125  and the measurement instrument  135 . In various implementations, one or more of: the user device  120 , the AIISMP  125 , the model database server  130 , or the measurement instrument  135  may include an application server configured to store or provide access to information used by the system. In some implementations, one or more application server may retrieve or manipulate information in storage devices and exchange information through the network cloud  115 . In various implementations, one or more of: the user device  120 , the AIISMP  125 , the model database server  130 , or the measurement instrument  135  may include various applications implemented as processor-executable program instructions. Various processor-executable program instruction applications may also be configured in some implementations, to manipulate information stored remotely and process and analyze data stored remotely across the network cloud  115  (for example, the Internet). According to an exemplary implementation, as shown in  FIG. 2 , exchange of information through the network cloud  115  or other network may occur through one or more high speed connections. In some cases, high speed connections may be over-the-air (OTA), passed through networked systems, directly connected to one or more network cloud  115  or directed through one or more router. In various implementations, one or more router may be optional, and other implementations in accordance with the present disclosure may or may not utilize one or more router. One of ordinary skill in the art would appreciate that there are numerous ways any or all of the depicted devices may connect with the network cloud  115  for the exchange of information, and implementations of the present disclosure are contemplated for use with any method for connecting to networks for the purpose of exchanging information. Further, while this application may refer to high speed connections, implementations of the present disclosure may be utilized with connections of any useful speed. In an implementation example, components or modules of the system may connect to one or more of: the user device  120 , the AIISMP  125 , the model database server  130 , or the measurement instrument  135  via the network cloud  115  or other network in numerous ways. For instance, a component or module may connect to the system i) through a computing device directly connected to the network cloud  115 , ii) through a computing device connected to the network cloud  115  through a routing device, or iii) through a computing device connected to a wireless access point. One of ordinary skill in the art will appreciate that there are numerous ways that a component or module may connect to a device via network cloud  115  or other network, and implementations of the present disclosure are contemplated for use with any network connection method. In various examples, one or more of: the user device  120 , the AIISMP  125 , the model database server  130 , or the measurement instrument  135  could include a personal computing device, such as a smartphone, tablet computer, wearable computing device, cloud-based computing device, virtual computing device, or desktop computing device, configured to operate as a host for other computing devices to connect to. One or more communications means of the system may be any circuitry or other means for communicating data over one or more networks or to one or more peripheral device attached to the system, or to a system module or component. Appropriate communications means may include, but are not limited to, wireless connections, wired connections, cellular connections, data port connections, Bluetooth® connections, near field communications (NFC) connections, or any combination thereof. One of ordinary skill in the art will appreciate that there are numerous communications means that may be utilized with implementations of the present disclosure, and implementations of the present disclosure are contemplated for use with any communications means. 
       FIG. 3  depicts a structural view of an exemplary AIISMP configured with an Augmented Physical Measurement and Simulation Environment Engine (APMSEE) programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented by artificial intelligence. In  FIG. 3 , the block diagram of the exemplary AIISMP  125  includes processor  305  and memory  310 . The processor  305  is in electrical communication with the memory  310 . The depicted memory  310  includes program memory  315  and data memory  320 . The depicted program memory  315  includes processor-executable program instructions implementing the APMSEE (Augmented Physical Measurement and Simulation Environment Engine)  325 . The illustrated program memory  315  may encode processor-executable program instructions configured to implement an OS (Operating System). The OS may include processor executable program instructions configured to implement various operations when executed by the processor  305 . The OS may be omitted. The illustrated program memory  315  may encode processor-executable program instructions configured to implement various Application Software. The Application Software may include processor executable program instructions configured to implement various operations when executed by the processor  305 . The Application Software may be omitted. In the depicted implementation, the processor  305  is communicatively and operably coupled with the storage medium  330 . In the depicted implementation, the processor  305  is communicatively and operably coupled with the I/O (Input/Output) interface  335 . In the depicted implementation, the I/O interface  335  includes a network interface. The network interface may be a wireless network interface. The network interface may be a Wi-Fi interface. The network interface may be a Bluetooth® interface. The AIISMP  125  may include more than one network interface. The network interface may be a wireline interface. The network interface may be omitted. The I/O interface  335  may include electronic circuitry designed to permit processor  305  communication and control of various measurement instruments. In the depicted implementation, the processor  305  is communicatively and operably coupled with the user interface  340 . In the depicted implementation, the processor  305  is communicatively and operably coupled with the multimedia interface  345 . In the illustrated implementation, the multimedia interface  345  includes interfaces adapted to input and output of audio, video, and image data. The multimedia interface  345  may include one or more still image camera or video camera. Useful examples of the illustrated AIISMP  125  include, but are not limited to, personal computers, servers, tablet PCs, smartphones, or other computing devices. Multiple AIISMP  125  devices may be operably linked to form a computer network in a manner as to distribute and share one or more resources, such as clustered computing devices and server banks/farms. Various arrangements of such general-purpose multi-unit computer networks suitable for implementations of the disclosure, their typical configuration, and standardized communication links are well known to one skilled in the art, as explained in more detail in the foregoing  FIG. 2  description. An exemplary AIISMP  125  design may be realized in a distributed implementation. Some AIISMP  125  designs may be partitioned between a client device, such as, for example, a phone, and a more powerful server system, as depicted, for example, in  FIG. 2 . A AIISMP  125  partition hosted on a PC or mobile device may choose to delegate some parts of computation, such as, for example, machine learning or deep learning, to a host server. A client device partition may delegate computation-intensive tasks to a host server to take advantage of a more powerful processor, or to offload excess work. Some AIISMP  125  devices may be configured with a mobile chip including an engine adapted to implement specialized processing, such as, for example, neural networks, machine learning, artificial intelligence, image recognition, audio processing, or digital signal processing. Such an engine adapted to specialized processing may have sufficient processing power to implement some AIISMP  125  features. However, an exemplary AIISMP  125  may be configured to operate on a device with less processing power, such as, for example, various gaming consoles, which may not have sufficient processor power, or a suitable CPU architecture, to adequately support AIISMP  125 . Various implementations configured to operate on a such a device with reduced processor power may work in conjunction with a more powerful server system. 
       FIG. 4  depicts an exemplary integrated simulation and measurement software architecture. In  FIG. 4 , the illustrated software architecture  400  includes the artificial intelligence (AI)  405  governing the simulation model  140  augmentation with parameter measurements. The simulation model  140  of the physical setup and device under test  110  may be generated as a function of the schematic  410  by the AI design  415 . In the depicted implementation, the AI  405  governs the model  140  selection and augmentation with parameter measurements captured by the measurement instrument  135  from the physical setup and device under test  110 . In the illustrated implementation, the model  140  augmented with the parameter measurements is prepared by post process  420  for data visualization  425  and presented as a synthesized integrated simulation and measurement environment in the graphical user interface  430 . 
       FIG. 5  depicts an exemplary measurement and simulation synthesis process model. In  FIG. 5 , the illustrated process V-model  500  defines the Simulation and Measurement Platform software design that integrates the measured parameters  150 , component simulation  505   a , sub-system simulation  505   b , and system simulation  505   c  to create the synthesized integrated simulation and measurement environment  155 . An exemplary software architecture is described with reference to  FIG. 4 . The design and usage of the depicted process V-model  500  disclosed herein is distinct from the design and usage of V-models known in technical fields related to systems development, at least because the depicted process V-model  500  defines an iterative process, in stark contrast with a more conventional systems development V-model, which may be interpreted only sequentially, for example, from left to right. In an illustrative example, a process defined by the depicted process V-model  500  diagram may be interpreted as beginning from the bottom of the V-model  500  with measurement setup behavioral modeling, and proceeding up in the V-model  500  diagram with component simulation  505   a , sub-system simulation  505   b , and system simulation  505   c . Thus, the depicted process V-model  500  defines a process iteratively validating and verifying at multiple levels from bottom to top, and augmenting the model until AI determines comparable results are achieved. 
     In the depicted implementation, the process V-model  500  includes the repetition of each step to fulfill the evaluation of validation and verification processes. In the illustrated implementation, the evaluation of validation and verification based on the behavioral model  140  is validated and verified based on shared physics and measurements  150  captured by the measurement instrument  135  from the device under test  110 . In the depicted implementation, the evaluation of validation and verification based on shared metrics is repeated for the component simulation  505   a , the sub-system simulation  505   b , and the system simulation  505   c  until comparable results are obtained. The determination that comparable results have been obtained between the system measurement and system simulation may be based on a quality criterion evaluated by an artificial intelligence. Additionally, verified measurement results can be used as inputs to simulation tools, to increase the accuracy and efficiency of the simulation process. In this process V-model  500  combining the measurement and simulation, the behavioral model  140  in simulation may be physics-based and share the same governing physics as the measurement setup. As shown in  FIG. 4 , the software with the help of AI, will fill the gap between simulation and measurement by linking the components of the physical model and the measurement setup. The software provides an APE (Augmented Physical Environment) to help the user to perform the measurement accurately. This criteria may be difficult to achieve in some simulation scenarios. In such conditions, more verification (under various conditions) may be needed to guarantee accurate behavior of the model within the scopes of the measurement of interest. The choice of metric also needs to be based on a quantity which is directly measurable (such as electromagnetic field strength, voltage, current, impedance, s-parameters, and the like). Under some circumstances, such as when the quantity of interest is indirectly measured, more verification may be needed. 
       FIG. 6  depicts an exemplary integrated simulation and measurement information flow. In  FIG. 6 , the exemplary Simulation and Measurement Platform information flow  600  depicts the integration of the physical quantity represented by the measured parameter  150  with simulation data generated based on the behavioral model  140 . The measured parameter  150  is integrated with the simulation data in in the virtual environment  605 . The measurement instrument  135  sensor converts the physical quantity to the electrical signal represented by measurement  145  data. Simulation  505  generates modeled data based on the behavioral model  140 . The measurement data and the modeled data are combined to form the synthesized integrated simulation and measurement environment information  610 . 
       FIG. 7  depicts a schematic view of an exemplary integrated simulation and measurement setup. In  FIG. 7 , the setup schematic  700  depicts an exemplary synthesis of measurement and simulation scenario. The software and hardware implementation of the exemplary measurement scenario is described. The goal of the illustrated scenario is to measure the transfer function of the DUT  110 . In the illustrated example, DUT  110  may be a radio frequency (RF) attenuator or amplifier. The transfer function of the DUT  110  will be measured using the measurement instrument  135 . In the depicted example, the measurement instrument  135  is a vector network analyzer (VNA). A VNA is a measurement instrument capable of measuring scattering parameters (S-parameters) of a device under test (DUT). In the depicted example, the loss of an attenuator or gain of an amplifier is measured based on measuring two port insertion loss (or gain) also known as S21 parameter as a function of frequency. The depicted setup schematic  700  of the physical measurement for this scenario includes input  705  of the DUT  110  connected to port 1  710  of the VNA and output  715  of DUT  110  connected to port 2  720  of the VNA using coaxial cables. Port  1   710  of the VNA transmits an RF signal toward the DUT  110  and port 2  720  of the VNA receives the attenuated (in case DUT  110  is an attenuator) or amplified (in case DUT  110  is an amplifier) signal from the DUT  110 . The ratio of the received voltage on port 2  720  over the transmitted voltage on port 1  710  is defined as S21 parameter. 
       FIG. 8  depicts a process flow of an exemplary APMSEE programmed and configured to synthesize an interactive simulation and measurement environment based on iteratively validating and verifying a physics-based model augmented based on artificial intelligence. In  FIG. 8 , the depicted method is given from the perspective of the Augmented Physical Measurement and Simulation Environment Engine (APMSEE)  325  implemented via processor-executable program instructions executing on the AIISMP  125  processor  305 , depicted in  FIG. 3 . In the illustrated implementation, the APMSEE  325  executes as program instructions on the processor  305  configured in the APMSEE  325  host AIISMP  125 , depicted in at least  FIG. 1 ,  FIG. 2 , and  FIG. 3 . In some implementations, the APMSEE  325  may execute as a cloud service communicatively and operatively coupled with system services, hardware resources, or software elements local to and/or external to the APMSEE  325  host AIISMP  125 . The illustrated process  800  is a non-limiting illustrative example of a Simulation and Measurement Platform implementation&#39;s measurement of the S21 parameter of an attenuator or amplifier using a VNA. Other measurements of other devices are contemplated, as would be recognized by one of ordinary skill. 
     The depicted method  800  begins at step  805  with the processor  305  performing a test to determine if the device under test is an attenuator or an amplifier. The determination may be based on measurement data evaluated as a function of an AI trained with simulated data generated by a model of a known device type. 
     Upon a determination by the processor  305  at step  805  the device under test is an attenuator, the method continues at step  810  with the processor  305  selecting an attenuator simulation model, and the method continues at step  820 . 
     Upon a determination by the processor  305  at step  805  the device under test is an amplifier, the method continues at step  815  with the processor  305  selecting an amplifier simulation model, and the method continues at step  820 . 
     At step  820  the processor  305  activates the trained AI to govern the Simulation and Measurement Platform measurement scenario based on modeled and measured parameters, and the method continues at step  825 . 
     At step  825 , the processor  305  receives from the AI hard limits for modeled and measured parameters determined by the AI. 
     At step  830 , the processor  305  sets measurement instrument parameters. In this example scenario, the software communicates with the VNA using a communication protocol such as Standard Commands for Programmable Instruments (SCPI). In some implementations, the user may set in the software that the parameter of interest is an S-parameter and the measurement instrument is a VNA. In this example, the AI configures the initial parameters in the software platform to transmit a pilot signal from port 1 and receive a signal from port 2 of the VNA. The measurement instrument parameters set by the processor  305  may be based on the hard parameter limits received by the processor  305  from the AI. The software may then be reconfigured according to the new information, with the capability of user interaction to change the parameters. This process may be implemented by machine learning algorithms such as an active learning model. The measurement instrument parameters set by the processor  305  may include, for example, Start frequency, Stop frequency, IF bandwidth, Power, Number of points, S-parameter, Sweep type, or other parameters as may be known to one of ordinary skill. 
     At step  835 , the processor  305  reads data from the instrument. The data read by the processor  305  from the instrument may be measurement data captured from the device under test. The processor  305  activates the AI to analyze the data read from the instrument. After the initial parameters on the instrument are set, the software reads the S-parameter of the DUT. Another layer of AI performs an analysis on the acquired data. This analysis is performed to confirm the measured S-parameter follows the expected values based on the simulated physical model. In this example, the AI analyzes the transmitted and received pilot signal to understand the physical property of the DUT, and match it to a physical component based on the simulated parameters of the DUT. In this scenario the simulation model of the attenuator is the mathematical attenuation factor of the magnitude of a sinusoidal signal on the output of the attenuator compared to the input of the attenuator. In the case of the amplifier, the simulation model is the mathematical amplification factor of the magnitude of a sinusoidal signal on the output of the amplifier compared to the input of the amplifier. In this case, the AI provides a probabilistic prediction of the type of the DUT and the probabilistic prediction of the parameters of the DUT such as gain or loss. The AI provides a confidence level for the predicted parameters based on the pilot signal. 
     At step  840 , the processor  305  performs a test to determine if the data read from the instrument by the processor  305  at step  835  matches the simulation model selected based on the determination by the processor  305  at step  805 . The model may be an attenuator or amplifier simulation model. Upon a determination by the processor  305  at step  840  the data read from the instrument matches the selected model, the method continues at step  845 . Upon a determination by the processor  305  at step  840  the data read from the instrument does not match the selected model, the processor  305  activates the AI to analyze the data, and the method continues at step  850 . 
     At step  845 , the processor  305  reads and saves the data read from the instrument, plots the data per the user&#39;s configuration, and the method ends. 
     At step  850 , the processor  305  performs a test to determine if a physical test setup issue has been detected, based on the AI data analysis performed by the processor  305  at step  840 . The AI may decide if the mismatch is due to improper parameters on the instruments, or due to an issue in the physical measurement setup. In the case of improper parameters, the AI may reconfigure the settings in the instrument, and repeat the data acquisition process described above. Upon a determination by the processor  305  at step  850  that a physical test setup issue has been detected, the method continues at step  860 . Upon a determination by the processor  305  at step  850  that a physical test setup issue has not been detected, the method continues at step  855 . 
     At step  855 , the processor  305  activates the AI to analyze the data read from the instrument, and the processor  305  changes the instrument parameters based on the AI data analysis. The method continues at step  830 . 
     At step  860 , the processor  305  indicates the user needs to change the physical setup. The processor  305  may notify the user to make proper modification in the setup, and the software may repeat the data acquisition process described above. The method continues at step  830 . 
     In some implementations, the method may repeat. In various implementations, the method may end. 
       FIG. 9  depicts an exemplary simulation and measurement configuration. In  FIG. 9 , the exemplary simulation and measurement configuration  900  depicts the software implementation configured to measure the loss of an RF attenuator or the gain of an amplifier visualized by the synthesized integrated simulation and measurement environment  155 . 
     The reference numbers and their respective elements depicted by the Drawings are summarized as follows.
           105  user     110  device under test (DUT)     115  network cloud     120  user device     125  AIISMP (Artificial Intelligence Integrated Simulation and Measurement Platform)     130  model database server     135  measurement instrument     140  model     145  measurement     150  parameter     155  synthesized integrated simulation and measurement environment     160  simulation and measurement synthesis step  160       165  simulation and measurement synthesis step  165       170  simulation and measurement synthesis step  170       175  simulation and measurement synthesis step  175       180  simulation and measurement synthesis step  180       185  simulation and measurement synthesis step  185       190  simulation and measurement synthesis step  190       195  simulation and measurement synthesis step  195       201  wireless access point     202  wireless link     203  router     204  communication link     205  communication link     206  wireless access point     207  wireless communication link     305  processor     310  memory     315  program memory     320  data memory     325  APMSEE (Augmented Physical Measurement and Simulation Environment Engine)     330  storage medium     335  I/O interface     340  user interface     345  multimedia interface     400  software architecture     405  artificial intelligence     410  schematic     415  artificial intelligence design     420  post process     425  data visualization     430  graphical user interface     500  process V-model     505  simulation     505   a  component simulation     505   b  sub-system simulation     505   c  system simulation     600  information flow     605  virtual environment     610  information     700  setup schematic     705  input     710  port 1     715  output     720  port 2     800  APMSEE process flow     900  simulation and measurement configuration       

     Although various features have been described with reference to the Drawings, other features are possible. 
     In the present disclosure, various features may be described as being optional, for example, through the use of the verb “may.” For the sake of brevity and legibility, the present disclosure does not explicitly recite each and every permutation that may be obtained by choosing from the set of optional features. However, the present disclosure is to be interpreted as explicitly disclosing all such permutations. For example, a system described as having three optional features may be implemented in seven different ways, namely with just one of the three possible features, with any two of the three possible features or with all three of the three possible features. The respective implementation features, even those disclosed solely in combination with other implementation features, may be combined in any configuration excepting those readily apparent to the person skilled in the art as nonsensical. 
     A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, the steps of the disclosed techniques may be performed in a different sequence, components of the disclosed systems may be combined in a different manner, or the components may be supplemented with other components. Accordingly, other implementations are contemplated, within the scope of the following claims.