Neural Network Optimization

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Period ending 2026-09-07

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A weekly snapshot of new work published in Neural Network Optimization.

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  1. Stochastic Optimization of Tree Tensor Networks

    Sep 1, 2026Marius Willner, Maximilian Scharf, André Uschmajew +2Tensor NetworksNeural Network Optimization

  2. Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization

    Aug 13, 2026Zhixin Ren, Yau Lyu, Congrong Li +2AdamNeural Network Optimization

  3. Accelerated Learning of High Dimensional Functions with a Tensor-Featured Training Network

    Aug 11, 2026Karl Pierce, Yuehaw Khoo, Haizhao YangLow-Rank Tensor DecompositionHigh-Dimensional

  4. MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries

    Aug 10, 2026Zijiang Yang, Xiaomeng Wu, Dongmei FuNeural OperatorsPartial Differential Equations

  5. Input convex neural networks as surrogates in mathematical optimisation

    Aug 10, 2026Yu Liu, Jan Kronqvist, Fabricio OliveiraMixed-Integer Linear ProgrammingSurrogate Gradient

  6. Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

    Aug 5, 2026Shengxin Kong, Liwen Xu, Jingwen FuNeural Partial Differential Equation SolversEvolutionary Search

  7. AOS: Adaptive Optimizer Switching via Training-State Signals for Faster Convergence and Better Generalization

    Aug 3, 2026Alok Kumar Pandey, Umang Chaturvedi, Aatish Rana +1Adaptive OptimizersNeural Network Optimization

  8. Variational Boosting for Physics-Informed Neural Networks

    Jul 27, 2026Pavlos Protopapas, Kaylee VoConventional Physics-Informed Neural NetworksNeural Network Optimization

  9. Operator Neural Jump ODEs: L^2-optimal prediction in function spaces

    Jul 25, 2026Florian Krach, Oliver Löthgren, Josef TeichmannNeural Ordinary Differential EquationsNeural-Network Approximation

  10. Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers

    Jul 24, 2026Zhangyong Liang, Huanhuan GaoNeural Partial Differential Equation SolversRiemannian Optimization

  11. Searching the Space of Feed-Forward Neural-Network Weight-Update Rules with Fixed Depth Symbolic Regression

    Jul 23, 2026Charles Brum, Edward FinkelsteinSymbolic RegressionNeural Network Optimization

  12. A Defense of the Quadratic Model

    Jul 23, 2026Alexandru Meterez, Pranav Ajit Nair, Depen Morwani +3Hessian EigenvalueNeural Network Optimization

  13. LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

    Jul 15, 2026Nilay Anurag, Shital Adhikari, Taniya Kapoor +1Conventional Physics-Informed Neural NetworksPartial Differential Equations

  14. Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions

    Jul 13, 2026Yongquan Shi, Zijing Ou, Shiping Wang +1Variational InferenceSurrogate Gradient

  15. Multi-dimensional training-priority weighting based on physical information propagation paths: a unified residual-weighting framework for physics-informed neural networks

    Jul 13, 2026Zhangyi Lian, Xinda Dong, Wenxuan Huo +3Conventional Physics-Informed Neural NetworksNeural Network Optimization

  16. An optimal control approach for neural network architecture adaptation with a posteriori error estimation

    Jul 8, 2026C G Krishnanunni, Thomas Scott, Tan Bui-ThanhNeural-Network ApproximationNeural Network Optimization

  17. The Anatomy of Implicit Bias: Information Allocation in Neural Network Training

    Jul 8, 2026Zhang Gongyue, Wang Zhiyong, Liu Donghan +3Neural Network OptimizationSpectral Preconditioning

  18. Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks

    Jul 5, 2026Shokhrukh Ibragimov, Arnulf JentzenNeural Network OptimizationAdam

  19. Optimality-Informed Neural Networks for Lunar Landing Trajectory Optimization

    Jul 2, 2026Zhenbo WangOptimal ControlGradient-Based Trajectory Optimization

  20. One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

    Jul 2, 2026Juan Agustín Duque, Sergio García Heredia, Vinicius Hernandes +4Quantum LearningMany-Body Physics

  21. An Optimisation Framework for the Well-Conditioned Training of Physics-Informed Neural Networks

    Jul 2, 2026Joseph Webb, Sadok Jerad, Coralia CartisNewtonNeural Network Optimization

  22. Neural Certificate Pricing for Combinatorial Optimization Problems

    Jul 1, 2026Jingyi Chen, Xinyuan Zhang, Xinwu QianCombinatorial OptimizationFinite-Sample Certificates

  23. GPU Parallelization Strategies for Forward and Backward Propagation in Shallow Neural Networks: A CUDA-Based Comparative Study

    Jun 29, 2026Rania Zitouni, Nadine Bousdjira, Sarah Hasnaoui +2CudaNeural Network Optimization

  24. Bilevel Optimization for Neural Architecture Search

    Jun 28, 2026Abhishek Shukla, Ankur Sinha, Faiz HamidBilevel OptimizationNeural Architecture Search

  25. FiRe: Frequency Reparameterization as a Preconditioner for Periodic Implicit Neural Representations

    Jun 28, 2026Harinandan Shukla, Rajarshi Verma, Jitin SinglaImplicit Neural RepresentationsReparameterization

  26. SOAP-Bubbles: Structured Weight Uncertainty for Neural Networks

    Jun 22, 2026Adrian Robert Minut, Nico Daheim, Marco Miani +3Bayesian Neural NetworksNeural Network Optimization

  27. GeoRouteNet: Geometry-Enhanced Non-Autoregressive Neural Solver for the Traveling Salesman Problem

    Jun 22, 2026Xiang LiSalesman ProblemNeural Solvers

  28. Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks

    Jun 18, 2026Fedor Buzaev, Dmitry Efremenko, Egor Bugaev +4Conventional Physics-Informed Neural NetworksHyperparameter

  29. Modularity-Free Conflict-Averse Training for Generalized PINNs

    Jun 18, 2026Heejo Kong, Beomchul Park, Sung-Jin Kim +1Conventional Physics-Informed Neural NetworksPartial Differential Equations

  30. Schattor: Schatten-family methods for deep learning optimization

    Jun 14, 2026Bohao Ma, Junyu Zhang, Chuan HeStochastic OptimizationNeural Network Optimization

  31. Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System

    Jun 13, 2026Abdeladhim Tahimi, Rinaldo Vieira da Silva JuniorConventional Physics-Informed Neural NetworksOptimal Control

  32. How Much Memory Do We Need? Adaptive Memory Gate for Neural Operators

    Jun 11, 2026Jihyeon Hur, Yongseok Kwon, Min-Gi Jo +2Neural OperatorsNeural Network Optimization

  33. Different Layers, Different Manifolds: Module-Wise Weight-Space Geometry in Transformer Optimization

    Jun 11, 2026Kirato YoshiharaManifoldsNeural Network Optimization

  34. Beyond the Thin-Layer Limit: Differentiable Volumetric Training for Visible-Range Diffractive Neural Networks

    Jun 5, 2026Dineth Jayakody, Dushan N. WadduwageDiffractDifferentiable Relaxation

  35. Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates

    Jun 5, 2026Julian Withöft, Werner John, Emre Ecik +2Neural Network OptimizationJoint Optimization

  36. Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks

    Jun 4, 2026Cornelius Otchere, Michael ShieldsNeural Network OptimizationFisher Information

  37. On the training of physics-informed neural operators for solving parametric partial differential equations

    Jun 4, 2026Nanxi Chen, Chuanjie Cui, Airong Chen +2Conventional Physics-Informed Neural NetworksPhysics-Informed Deep Learning

  38. LLM-Guided ANN Index Optimization for Human-Object Interaction Retrieval

    Jun 3, 2026Shahrzad Esmat, Chaunte W. Lacewell, Sameh Gobriel +2HyperparameterMemory-Augmented Large Language Models

  39. Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

    Jun 3, 2026Ammar Hoori, Yuichi MotaiRadial Basis FunctionParticle Swarm Optimization

  40. Curvature-aware dynamic precision approach for physics-informed neural networks

    Jun 3, 2026Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn +1Conventional Physics-Informed Neural NetworksCurvature Prediction Quality

  41. Loss-Conditional PINNs for Parametric PDE Families

    Jun 3, 2026Anna Lazareva, Alexander TarakanovConventional Physics-Informed Neural NetworksNeural Network Optimization

  42. Singularity-aware Optimization via Randomized Geometric Probing: Towards Stable Non-smooth Optimization

    May 28, 2026Ruoran Xu, Borong She, Xiaobo Jin +1AdamNeural Network Optimization

  43. Insurance Pricing Optimization via Off-Policy Evaluation

    May 27, 2026Sascha Günther, Dimitri Semenovich, Mario V. WüthrichPricesPolicy Optimization

  44. GeneralThinker: Domain-General Reasoning through Likelihood-Guided Answer-Conditioned Optimization

    May 27, 2026Shengmin Piao, Sanghyun ParkThinkingNeural Network Optimization

  45. EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization

    May 25, 2026Chung-Yiu Yau, Dawei Li, Athanasios Glentis +3NesterovNeural Network Optimization

  46. Label-NTK Alignments and A Tighter Convergence Bound in the NTK Regime

    May 24, 2026Ruchirinkil Marreddy, Chaoyue LiuNeural Tangent KernelOver-Parameterized Neural Networks

  47. A lift for input-convex neural network training

    May 22, 2026Ali Siahkoohi, Anirudh ThatipelliConvex LossNeural Network Optimization

  48. Optimization of randomized neural networks for transfer operator approximation

    May 22, 2026Mohammad Tabish, Stefan KlusNeural-Network ApproximationNeural Network Optimization

  49. Non-normal spectral signatures of instability in neural network training dynamics

    May 22, 2026Souvik GhoshNeural Network OptimizationHessian Eigenvalue

  50. StableGrad: Backward Scale Control without Batch Normalization

    May 19, 2026Jose I. Mestre, Alberto Fernández-Hernández, Cristian Pérez-Corral +2Batch NormalizationNeural Network Optimization

  51. Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise

    May 18, 2026Jiayu Zhang, Tianyi LinNeural Network OptimizationStochastic Gradient Descent

  52. AOT-POT: Adaptive Operator Transformation for Large-Scale PDE Pre-training

    May 15, 2026Qitan Lv, Hong Wang, Zhongkai Hao +5Neural OperatorsPartial Differential Equations

  53. A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training

    May 14, 2026Andrea Angino, Bindi Çapriqi, Shega Likaj +2Spectral PreconditioningNeural Network Optimization

  54. NeuroRisk: Physics-Informed Neural Optimization for Risk-Aware Traffic Engineering

    May 13, 2026Yingming Mao, Ximeng Liu, Jingyi Cheng +8Forward-Looking Fault-Risk PredictionNeural Network Optimization

  55. Implicit Neural Optimal Transport via Fixed-Point Optimization

    May 11, 2026Yesom Park, Eric Gelphman, Stanley Osher +1Optimal TransportNeural Network Optimization

  56. Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs

    May 11, 2026Hoyeol Yoon, Seoungbin Bae, Nam Ho-Nguyen +1Conventional Physics-Informed Neural NetworksPartial Differential Equations

  57. A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting

    May 9, 2026Runyao Yu, Julia Lin, Derek W. Bunn +6Electricity MarketsEnergy Forecasting

  58. AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training

    May 8, 2026Binghang Lu, Runyu Zhang, Changhong Mou +2Conventional Physics-Informed Neural NetworksNeural Network Optimization

  59. QuadNorm: Resolution-Robust Normalization for Neural Operators

    May 8, 2026Bum Jun Kim, Makoto Kawano, Yusuke Iwasawa +1Batch NormalizationNeural Operators

  60. Directional Consistency as a Complementary Optimization Signal: The GONO Framework

    May 7, 2026Victor Daniel GeraNeural Network Optimization