CALIBRATION USING DIFFERENTIAL MACHINE LEARNING

1 . A method comprising:

identifying, by a framework and based on a model script, a model that generates an output based on a set of inputs, wherein the inputs include a plurality of parameters;

selecting, by the framework, a first plurality of parameter values;

assembling, by the framework, a set of training samples by observing outputs generated by the model in response to each of the first plurality of parameter values;

training, by the framework and based on the set of training samples, a surrogate model, wherein the surrogate model is trained to predict outputs of the model;

generating, by the framework and using the surrogate model, predicted outputs of the model, wherein each of the predicted outputs of the model is based on a different parameter value in a second plurality of parameter values;

selecting, by the framework and based on the predicted outputs of the model, a desired parameter value; and

applying the model, using the desired parameter value, to predict a value of interest for an input value.

2 . The method of claim 1 , further comprising:

receiving, by a control system, the value of interest;

interpreting, by the control system, the value of interest to determine an action to take; and

outputting, by the control system and over a network to a downstream system, a control signal to control the operation of the downstream system.

3 . The method of claim 1 , wherein selecting the first plurality of parameter values includes:

performing adaptive sampling to mitigate effects caused by variances across the first plurality of parameter values.

4 . The method of claim 1 , wherein assembling the set of training samples includes:

assembling training samples that each include a state value, a parameter value of the first plurality of parameter values, the observed output value, and a derivative of the observed output value with respect to the parameter value.

5 . The method of claim 1 , wherein training the surrogate model includes:

training a deep neural network using least square regression regularized with derivatives of the observed output values with respect to parameter values.

6 . The method of claim 1 , wherein training the surrogate model includes:

training a plurality of surrogate models, where each surrogate model is trained starting with a different random seed.

7 . The method of claim 6 , wherein generating predicted outputs of the model includes:

executing each of the surrogate models in parallel to generate different sets of predicted outputs of the model.

8 . The method of claim 7 , wherein selecting a desired parameter value includes:

selecting a desired one of the plurality of surrogate models based on an assessment of the robustness of the predicted outputs generated by each of the plurality of surrogate models; and

selecting the desired parameter value based on the predicted outputs generated by the desired surrogate model.

9 . The method of claim 1 , wherein selecting the desired parameter value includes:

selecting an optimal parameter value from the second plurality of parameter values, wherein the optimal parameter value tends to maximize the output from the model.

10 . The method of claim 1 , wherein predicting the value of interest includes:

predicting a payoff of an interest rate option contract.

11 . A computing system comprising processing circuitry and a storage device, wherein the processing circuitry has access to the storage device and is configured to:

identify, based on a textual description, a model that generates an output based on a set of inputs, wherein the inputs include a plurality of parameters;

select a first plurality of parameter values;

assemble a set of training samples by observing outputs generated by the model in response to each of the first plurality of parameter values;

train, based on the set of training samples, a surrogate model, wherein the surrogate model is trained to predict outputs of the model;

generate, using the surrogate model, predicted outputs of the model, wherein each of the predicted outputs of the model is based on a different parameter value in a second plurality of parameter values;

select, based on the predicted outputs of the model, a desired parameter value; and

apply the model, using the desired parameter value, to predict a value of interest for an input value.

12 . The computing system of claim 11 , wherein the processing circuitry is further configured to:

receive the value of interest;

interpret the value of interest to determine an action to take; and

output, to a downstream system, a control signal to control the operation of the downstream system.

13 . The computing system of claim 11 , wherein to select the first plurality of parameter values, the processing circuitry is further configured to:

perform adaptive sampling to mitigate an effect caused by variances across the first plurality of parameter values.

14 . The computing system of claim 11 , wherein to assemble the set of training samples, the processing circuitry is further configured to:

assemble training samples that each include a state value, a parameter value of the first plurality of parameter values, the observed output value, and a derivative of the output value with respect to the parameter value.

15 . The computing system of claim 11 , wherein to train the surrogate model, the processing circuitry is further configured to:

train a deep neural network using least square regression regularized with derivatives of the observed output values with respect to the first plurality of parameter values.

16 . The computing system of claim 11 , wherein to train the surrogate model, the processing circuitry is further configured to:

train a plurality of surrogate models, where each is trained starting with a different random seed.

17 . The computing system of claim 16 , wherein to generate predicted outputs of the model, the processing circuitry is further configured to:

execute each of the surrogate models in parallel to generate different sets of predicted outputs of the model.

18 . The computing system of claim 17 , wherein to select a desired parameter value, the processing circuitry is further configured to:

select a desired one of the plurality of surrogate models based on an assessment of the robustness of the predicted outputs generated by each of the plurality of surrogate models; and

select the desired parameter value based on the predicted outputs generated by the desired surrogate model.

19 . The computing system of claim 11 , wherein to select the desired parameter value, the processing circuitry is further configured to:

select an optimal parameter value from the second plurality of parameter values.

20 . A non-transitory computer-readable medium comprising instructions that, when executed, configure processing circuitry of a computing system to:

identify, based on a textual description, a model that generates an output based on a set of inputs, wherein the inputs include a plurality of parameters;

select a first plurality of parameter values;

assemble a set of training samples by observing outputs generated by the model in response to each of the first plurality of parameter values;

train, based on the set of training samples, a surrogate model, wherein the surrogate model is trained to predict outputs of the model;

generate, using the surrogate model, predicted outputs of the model, wherein each of the predicted outputs of the model is based on a different parameter value in a second plurality of parameter values;

select, based on the predicted outputs of the model, a desired parameter value; and

apply the model, using the desired parameter value, to predict a value of interest for an input value.