plasticity
PulseAugur coverage of plasticity — every cluster mentioning plasticity across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New genetic programming method enables lifelong learning for job shop scheduling
Researchers have developed a novel surrogate-assisted genetic programming method to enable lifelong learning for dynamic flexible job shop scheduling (DFJSS). This approach aims to overcome the limitations of traditiona…
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New research reveals gradient flow can mispredict neural network adaptation with finite-batch SGD
A new paper explores how population gradient flow, a common method for analyzing neural network adaptation, can inaccurately predict the behavior of finite-batch stochastic gradient descent (SGD). The research identifie…
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New Cluster Attention Neural Operator Solves Parametric PDEs
Researchers have introduced the Cluster Attention Neural Operator (CANO), a novel approach to solving parametric partial differential equations (PDEs). Unlike existing methods that can suffer from quadratic complexity o…
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New Bellman Equation Targets Plasticity in Reinforcement Learning
Researchers have introduced a new Bellman optimality equation specifically designed to optimize plasticity in continual reinforcement learning. This work builds upon a prior formalization that reframed the stability-pla…
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New AI framework models material behavior using neural operators and causal attention
Researchers have developed a novel data-driven framework for modeling the constitutive behavior of materials, particularly for path-dependent inelastic materials. This approach treats a deforming material as a functiona…
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AI models learn constitutive laws in mechanics and thermomechanics
Researchers have developed novel physics-informed neural network frameworks for discovering constitutive models in mechanics. One approach focuses on identifying anisotropic yield functions in plasticity by representing…
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Modularity's Role in Continual Learning Explored in New AI Research
Two new research papers explore the role of modularity in continual learning, a field focused on enabling AI systems to learn new information without forgetting previous knowledge. One paper, "Dimensionality Controls Wh…