Data modeling using elastic weight consolidation physics-informed neural networks
AssigneeInternational Business Machines Corporation
InventorsSrinath Madasu, SEAN WAGNER
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Abstract
According to one embodiment, a method, computer system, and computer program product for implementing a deep physics neural network for modeling data using a hybrid training approach is provided. The present invention may include training a physics-informed neural network (PINN) by incorporating a mathematical equation model, and its boundary and initial conditions, into the PINN; pre-training an elastic weight consolidation physics-informed neural network (EWC-PINN) using a transfer learning method; and training the EWC-PINN using noisy training data.
Claims20
A computer-implemented method for implementing a deep physics neural network for modeling data using a hybrid training approach, the method comprising:
- training a physics-informed neural network (PINN) by incorporating a mathematical equation model, and its boundary and initial conditions, into the PINN;
- pre-training an elastic weight consolidation physics-informed neural network (EWC-PINN) using a transfer learning method;
- and
- training the EWC-PINN using noisy training data.
A computer system for implementing a deep physics neural network for modeling data using a hybrid training approach, the computer system comprising:
- one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
- training a physics-informed neural network (PINN) by incorporating a mathematical equation model, and its boundary and initial conditions, into the PINN;
- pre-training an elastic weight consolidation physics-informed neural network (EWC-PINN) using a transfer learning method;
- and
- training the EWC-PINN using noisy training data.
A computer program product for implementing a deep physics neural network for modeling data using a hybrid training approach, the computer program product comprising:
- one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor to cause the processor to perform a method comprising:
- training a physics-informed neural network (PINN) by incorporating a mathematical equation model, and its boundary and initial conditions, into the PINN;
- pre-training an elastic weight consolidation physics-informed neural network (EWC-PINN) using a transfer learning method;
- and
- training the EWC-PINN using noisy training data.
Dependencies are inferred from the claim text.