#automatic-differentiation News & Analysis

3 articles tagged with #automatic-differentiation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · Jun 96/10

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Systematic LLM Translation of Legacy Scientific Code to Differentiable Frameworks: Application to a Land Surface Model

Researchers developed an LLM-based pipeline that automatically translates legacy Fortran scientific code into JAX, a differentiable programming framework. Applied to a 19,000-line land surface model, the approach achieved 24x speedup and 8x faster parameter optimization while enabling gradient-based analysis through automatic differentiation.

AINeutralarXiv – CS AI · Jun 56/10

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Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data

Researchers have developed FE-MAD, a differentiable machine learning framework that integrates neural networks into finite element solvers to identify material properties from experimental deformation data. The method combines the flexibility of neural networks with the physical rigor of finite element analysis, demonstrated on hyperelastic material characterization across multiple experimental datasets without requiring manual surrogate models or analytic adjoints.

AINeutralarXiv – CS AI · Jun 26/10

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Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks

Researchers introduce Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a novel technique for compressing deep neural networks by building large weight tensors from hierarchical small cores with nonlinear activations. The method achieves compression ratios from 2,000× to 77,000× on standard architectures like AlexNet and VGG-16 while maintaining or improving accuracy, representing a mathematically structured approach to reducing model size.