updating paper pdf

Author: rsmath

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+I"X<p>We propose a Knowledge Management Framework based on NLP techniques that would tackle challenges such as resource-intensive nature, lack of comprehensive information sources, and overlapping jurisdictions in policy analysis. The framework is designed to be platform-, language- and policy-agnostic. To classify financial incentives in restoration policies, both Sentence-BERT and Cross-Encoders performed well. For sentence classification inference with Sentence-BERT, a random forest classifier can be used to assign a category to a given sentence using the Sentence-BERT learned embeddings.</p>
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+I"�<p>We present a new technique for the accelerated training of physics-informed neural networks (PINNs): discretely-trained PINNs (DT-PINNs). DT-PINNs are trained by replacing exact spatial derivatives with high-order accurate numerical discretizations computed using meshless radial basis function-finite differences (RBF-FD) and applied via sparse-matrix vector multiplication. Additionally, though traditional PINNs (vanilla-PINNs) are typically stored and trained in 32-bit floating-point (fp32) on the GPU, we show that for DT-PINNs, using fp64 on the GPU leads to significantly faster training times than fp32 vanilla-PINNs with comparable accuracy. Our results show that fp64 DT-PINNs offer a superior cost-accuracy profile to fp32 vanilla-PINNs.</p>
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