Surrogate Neural Networks for Black-Box Inverse Simulation
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Solution Overview
Problem
Solving inverse problems for black-box simulators, particularly those that are non-differentiable, is challenging due to the difficulty in obtaining simulator gradients and the need to minimize the number of simulator calls, which is exacerbated by the complexity and opacity of these models.
Innovation Solution
Utilizing reinforcement learning to train a differentiable surrogate model that approximates the black-box simulator, allowing gradient-based optimization through a trained active learning policy to minimize simulator calls and optimize simulation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gradient-based optimization is used to solve inverse problems, then optimization efficiency is improved, but the ability to handle non-differentiable black-box simulators deteriorates
Solution Approach 1:
The patent creates a surrogate model that copies the behavior of the black-box simulator through reinforcement learning. This surrogate model is differentiable and can be used for gradient-based optimization, while the original non-differentiable simulator is only called for reward computation during training. This resolves the contradiction by providing a differentiable proxy that enables efficient optimization without requiring the original simulator to be differentiable.
Solution Approach 2:
The surrogate model acts as an intermediary between the non-differentiable black-box simulator and the gradient-based optimization process. It translates the non-differentiable simulator's outputs into a differentiable representation that can be differentiated with respect to simulation parameters, enabling gradient flow while maintaining compatibility with the original non-differentiable simulator.
2Loss of energy
If the number of simulator calls is reduced, then computational cost is decreased, but the accuracy of inverse problem solutions may deteriorate
Solution Approach 1:
The surrogate model is trained in advance using a limited number of simulator calls to capture the essential behavior of the black-box simulator. Once trained, this pre-computed model can be used repeatedly for optimization without additional simulator calls, achieving both low computational cost and high accuracy through the preliminary training phase.
Solution Approach 2:
The surrogate model creates a computational copy of the simulator's behavior that can be evaluated multiple times without calling the original simulator. This copy preserves the essential dynamics and relationships captured during training, enabling accurate inverse problem solutions with minimal simulator calls.
3Productivity
If a surrogate model is trained to approximate the simulator, then the number of simulator calls is reduced, but the complexity of the training process increases
Solution Approach 1:
The reinforcement learning agent automatically learns the optimal policy for when to call the simulator versus when to use the surrogate model. The training process self-adjusts based on the balance between accuracy and computational cost, eliminating the need for manual tuning of training complexity while achieving efficient simulator call reduction.
Solution Approach 2:
The reinforcement learning framework provides feedback during training about the performance of the surrogate model versus direct simulator calls. This feedback mechanism enables the agent to learn when the surrogate is sufficient and when direct simulator calls are necessary, automatically managing training complexity while reducing overall simulator calls.
Data Source
AI summary
Systems and techniques are provided for optimizing simulation parameters of non-differentiable simulators. A surrogate neural network trained to approximate a simulator model can generate one or more surrogate predictions based on processing a first set of parameter values and stochastic input data associated with the simulator model. A state vector indicative of the first set of parameter values and the one or more surrogate predictions can be generated, and used to determine an action corresponding to a trained agent of a reinforcement learning (RL)-based policy network, wherein the action is indicative of a decision to re-train the surrogate neural network or a decision not to re-train the surrogate neural network. A second set of parameter values corresponding to the parameters of the simulator model can be generated by updating the first set of parameter values using the action and one or more gradients determined for the surrogate neural network.