Abstract We generalize a magnetogram-matching Biot–Savart law (BSl) from planar to spherical geometry. For a given coronal current densityJ, this law determines the magnetic field whose radial component vanishes at the surface. The superposition of with a potential field defined by a given surface radial field,Br, provides the entire configuration whereBrremains unchanged by the currents. Using this approach, we (1) upgrade our regularized BSls for constructing coronal magnetic flux ropes (MFRs) and (2) propose a new method for decomposing a measured photospheric magnetic field as , where the potential,Bpot, toroidal,BT, and poloidal, , fields are determined byBr,Jr, and the surface divergence ofB–Bpot, respectively, all derived from magnetic data. OurBTis identical to the one in the alternative Gaussian decomposition by P. W. Schuck et al., whileBpotand are different from their poloidal fields and , which arepotentialin the infinitesimal proximity to the upper and lower side of the surface, respectively. In contrast, our has no such constraints and, asBpotandBT, refers to thesameupper side of the surface. In spite of these differences, for a continuousJdistribution across the surface,Bpotand are linear combinations of and . We demonstrate that, similar to the Gaussian method, our decomposition allows one to identify the footprints and projected surface-location of MFRs in the solar corona, as well as the direction and connectivity of their currents.
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SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression
Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoiet al2024EPJ Web Conf.29509036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).
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- Award ID(s):
- 2019786
- PAR ID:
- 10568896
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2632-2153
- Format(s):
- Medium: X Size: Article No. 015021
- Size(s):
- Article No. 015021
- Sponsoring Org:
- National Science Foundation
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