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Found 7 papers, 4 papers with code
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
Transformer-based Planning for Symbolic Regression
Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity, as external sources of knowledge into the transformer-based equation generation process.
Transformer for Partial Differential Equations' Operator Learning
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions.
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization.
Graph Neural Networks Accelerated Molecular Dynamics
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter.
Graph Convolutional Neural Networks for Body Force Prediction
The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements.
Data-driven Identification of 2D Partial Differential Equations using extracted physical features
Many scientific phenomena are modeled by Partial Differential Equations (PDEs).
