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Found 4 papers, 2 papers with code
ParFam -- Symbolic Regression Based on Continuous Global Optimization
In this paper, we present our new method ParFam that utilizes parametric families of suitable symbolic functions to translate the discrete symbolic regression problem into a continuous one, resulting in a more straightforward setup compared to current state-of-the-art methods.
On the Universal Transformation of Data-Driven Models to Control Systems
In other words, surrogate modeling for autonomous systems is much easier than for control systems.
On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning (e. g., for the training of neural networks).
Deep Model Predictive Control with Online Learning for Complex Physical Systems
The control of complex systems is of critical importance in many branches of science, engineering, and industry.
