US-20260127341-A1 - PHYSICS-INFORMED INTELLIGENT COMPUTATIONAL MODEL BASED ON SENSOR DATA

Abstract

Physics-based intelligent machine learning based computational modeling of a complex natural phenomenon that uses sensor data as input is disclosed. The computational modeling includes computing sensor performance characteristics of a physical sensor used in measuring attributes of a physical system. The modeling also includes simulating, based on a process-based model, the physical system to produce simulated data corresponding to one or more physical state variables of the natural system, and applying, to the simulated data, the computed sensor performance characteristics of the physical sensor to corrupt the simulated data to generate one or more simulated sensor responses that more closely approximates an actual output of the physical sensor. A training dataset is generated from the simulated data, which reflects the simulated sensor responses, and input parameters for the process-based model to train a machine learning model.

Inventors

• Stephen P. Farrington
• Andrea R. PEARCE

Assignees

• Transcend Engineering and Technology, LLC

Dates

Publication Date

20260507

Application Date

20251219

Claims (1)

1. 1 . A method comprising: establishing sensor performance characteristics of a physical sensor used in measuring an attribute of a physical system; simulating, by providing input parameters to a process-based model, the physical system to produce simulated data corresponding to state variables of the physical system; replacing, in the simulated data, at least one state variable corresponding to the attribute of the physical system, with a simulated sensor response by applying, to the at least one state variable, the sensor performance characteristics of the physical sensor to corrupt the at least one state variable; and generating a training dataset that includes the simulated data and the input parameters of the process-based model, wherein at least one physical system property of the input parameters or at least one state variable is identified as a training target.

Description

RELATED APPLICATIONS This application is a continuation of International Application No. PCT/US2024/034863, filed June 20, 2024, titled “Physics-Informed Intelligent Computational Model Based On Sensor Data,” which claims priority to U.S. Provisional Application No. 63/521,974, filed June 20, 2023, titled “Intelligent Computational Model Based on Sensor Data,” each of which is incorporated herein by reference in its entirety. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT Aspects of this disclosure were made with U.S. Government support under a contract awarded by the US Army Corps of Engineers, contract # W913E519C0003. The government has certain rights in the disclosure. TECHNICAL FIELD The present disclosure generally relates to an intelligent computational model, and more particularly, to developing a machine learning based computational model of a complex physical phenomenon that uses sensor data as input. In some implementations, the disclosure relates to developing a machine learning based computational model of attributes of fluid flow in unsaturated porous media that uses sensor data as input. BACKGROUND In machine learning, training data may be used to train a machine learning model. Obtaining training data for machine learning may entail some human input. Depending on the machine learning techniques and the kind of model being trained the amount and quality of training data may vary. By giving a machine learning model training data and modifying its parameters to reduce the error between the anticipated output and the actual output, a machine learning model is trained. This process may be performed numerous times until the model reaches an acceptable level of accuracy, driven by an optimization algorithm such as gradient descent or stochastic gradient descent using backpropagation. SUMMARY According to an aspect of the present disclosure, a method describing an intelligent machine learning based computational modeling of a complex physical phenomenon is disclosed. The method uses sensor data as input. The method includes computing sensor performance characteristics of a physical sensor used in measuring attributes of a physical system. The method also includes simulating, based on a process-based model, the physical system to produce simulated data corresponding to one or more physical state variables of the natural system, and applying, to the simulated data, the computed sensor performance characteristics of the physical sensor to corrupt the simulated data to generate one or more simulated sensor responses that more closely approximates an actual output of the physical sensor. In some cases, the physical phenomenon may be a natural phenomenon. A training dataset is generated to train a machine learning model, the training dataset is generated using the simulated sensor responses, the simulated data, and/or inputs of the process-based model. In some implementations, the simulated sensor responses may replace, in the training dataset, simulated data from which the simulated sensor responses were produced. Put another way, a simulated sensor response is produced by applying at least a sensor performance characteristic to a state variable represented in the simulated data; that state variable may be replaced by the simulated sensor response in the training data or that state variable may be replaced in the simulated data before storing the simulated data in the training dataset. The training dataset may include a plurality of training examples. Thus, as used herein, the simulated data stored in the training dataset reflects any simulated sensor responses generated from the simulated data. Each training example may identify an attribute of the physical system as a training target. The training target may be a physical system property. The training target may be a state variable. In some implementations, a simulated sensor response may be identified as a training target. The training example may identify multiple state variables as target training targets. Each training example may include a time series of simulation data for the training target(s). Each training example may include an instance of a time series of simulation data. The process-based model may be a virtual replica of the physical system that uses realistic data and/or other input data to mimic behavior of the physical system. The physical sensor can be one or a plurality of sensors of the same type that work to measure a given state variable of the physical system or one of a plurality of different types that work to measure one or more state variables of the physical system. In one implementation, the method includes performing the simulating step and the applying step a number of times, each of the number of times corresponding to a scenario. Each scenario may be defined by a number of input parameters representing attributes of the physical system being simulated. The attributes of a physical system can be invarian