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ISR: Invertible Symbolic Regression
The proposed ISR method naturally combines the principles of Invertible Neural Networks (INNs) and Equation Learner (EQL), a neural network-based symbolic architecture for function learning.
Data-Driven Discovery of PDEs via the Adjoint Method
In particular, for a family of parameterized and nonlinear PDEs, we show how the corresponding adjoint equations can be derived.
Gaussian Process Regression for Maximum Entropy Distribution
Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems.
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
We validate the proposed MESSY estimation method against other benchmark methods for the case of a bi-modal and a discontinuous density, as well as a density at the limit of physical realizability.
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