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Found 3 papers, 1 papers with code
Sym-Q: Adaptive Symbolic Regression via Sequential Decision-Making
Our results demonstrate that Sym-Q excels not only in recovering underlying mathematical structures but also uniquely learns to efficiently refine the output expression based on reward signals, thereby discovering underlying expressions.
Collective Relational Inference for learning heterogeneous interactions
First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time.
Learning Physics-Consistent Particle Interactions
Access to the governing particle interaction law is fundamental for a complete understanding of such systems.
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