A variational reduced-order model bridges perturbation and variational fracture approaches to simulate coplanar 3D crack propagation in heterogeneous brittle solids, uncovering size-dependent weakening-to-toughening crossovers driven by depinning instabilities.
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12 Pith papers cite this work, alongside 473 external citations. Polarity classification is still indexing.
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cs.LG 4 astro-ph.SR 1 cond-mat.mtrl-sci 1 cs.CE 1 cs.DC 1 cs.NE 1 cs.NI 1 math.OC 1 physics.chem-ph 1years
2026 12representative citing papers
SHAPE lifts gradient descent to an augmented phase space with a learned Hamiltonian vector field and event-triggered port updates to balance descent, exploitation, and exploration, improving best-so-far performance over fixed-policy methods in nonconvex tasks.
ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
Matrix-valued optimism equals matrix-valued augmentation additively for symmetric parameters, enabling closed-form hybrid designs that improve finite-step feasibility in constrained optimization.
SGD, approximations of Newton's method, natural gradient descent, and Adam are proven compatible with evolutionary dynamics when augmented with DLS noise, turning them into valid in silico simulations of asexual Darwinian evolution.
A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
New gas-phase measurements of C 1s binding energies in anthrone agree with ΔSCF calculations, and a benchmark of 44 core levels in molecules with 10-40 atoms yields a mean absolute error of 0.19 eV.
A sequential topology optimization approach uses SIMP results to initialize level-set refinement via signed distance function transfer on 3D meshes, achieving comparable compliance with up to 4.6x speedup on benchmarks.
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.
Introduces the Feasible Sovereign Operating Region (FSOR) as a construct for workloads sustainable under physical and regulatory limits, along with a joint compute-network optimization framework that enforces sustainability as hard constraints.
This survey organizes LLM optimizer literature into categories and argues the field is shifting toward rigorous, multi-factor comparisons of convergence, memory, stability, and complexity.
citing papers explorer
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Bridging perturbation and variational approaches in brittle fracture
A variational reduced-order model bridges perturbation and variational fracture approaches to simulate coplanar 3D crack propagation in heterogeneous brittle solids, uncovering size-dependent weakening-to-toughening crossovers driven by depinning instabilities.
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When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
SHAPE lifts gradient descent to an augmented phase space with a learned Hamiltonian vector field and event-triggered port updates to balance descent, exploitation, and exploration, improving best-so-far performance over fixed-policy methods in nonconvex tasks.
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ADELIA: Automatic Differentiation for Efficient Laplace Inference Approximations
ADELIA is the first AD-enabled INLA system that computes exact hyperparameter gradients via a structure-exploiting multi-GPU backward pass, delivering 4.2-7.9x per-gradient speedups and 5-8x better energy efficiency than finite differences on models with up to 1.9 million latent variables.
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Matrix-Valued Optimism is Matrix-Valued Augmentation: Additive Hybrid Designs for Constrained Optimization
Matrix-valued optimism equals matrix-valued augmentation additively for symmetric parameters, enabling closed-form hybrid designs that improve finite-step feasibility in constrained optimization.
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Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles
SGD, approximations of Newton's method, natural gradient descent, and Adam are proven compatible with evolutionary dynamics when augmented with DLS noise, turning them into valid in silico simulations of asexual Darwinian evolution.
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A second-order method landing on the Stiefel manifold via Newton$\unicode{x2013}$Schulz iteration
A second-order method achieves local quadratic convergence on the Stiefel manifold without retractions by combining a modified Newton tangent step with Newton-Schulz normal steps for constraint satisfaction.
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Learning Material-Aware Hamiltonian Risk Fields for Safe Navigation
A learned context-energy term in port-Hamiltonian policies creates selective risk navigation that activates evasive forces only when safer paths are available.
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Does the total energy difference method for modelling core level photoemission fail for bigger molecules?
New gas-phase measurements of C 1s binding energies in anthrone agree with ΔSCF calculations, and a benchmark of 44 core levels in molecules with 10-40 atoms yields a mean absolute error of 0.19 eV.
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Sequential topology optimization: SIMP initialization for level-set boundary refinement
A sequential topology optimization approach uses SIMP results to initialize level-set refinement via signed distance function transfer on 3D meshes, achieving comparable compliance with up to 4.6x speedup on benchmarks.
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PISP: Projected-Space Inference of Stellar Parameters
PISP projects high-dimensional spectra into optimized subspaces using PCA or active subspaces plus L1 selection to raise accuracy and speed of stellar parameter inference over standard methods.
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Sustainability-Constrained Workload Orchestration for Sovereign AI Infrastructure: A Joint Compute-Network Optimization Framework
Introduces the Feasible Sovereign Operating Region (FSOR) as a construct for workloads sustainable under physical and regulatory limits, along with a joint compute-network optimization framework that enforces sustainability as hard constraints.
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Navigating LLM Valley: From AdamW to Memory-Efficient and Matrix-Based Optimizers
This survey organizes LLM optimizer literature into categories and argues the field is shifting toward rigorous, multi-factor comparisons of convergence, memory, stability, and complexity.