NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A DFL-TFC framework with orthogonal polynomial expansion and domain mapping solves the governing DE for bending of exponentially loaded tapered perforated beams more efficiently than PINNs.
RBF is a hybrid edge-HPC architecture that decouples low-latency edge inference using surrogate models from asynchronous HPC-driven model updates for simulation-bounded cyber-physical systems.
citing papers explorer
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NLPOpt-Net: A Learning Method for Nonlinear Optimization with Feasibility Guarantees
NLPOpt-Net is an unsupervised neural architecture that learns parametric solutions to constrained NLPs by pairing a backbone network with quadratic projection layers that guarantee feasibility and near-zero constraint violations.
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Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam
A DFL-TFC framework with orthogonal polynomial expansion and domain mapping solves the governing DE for bending of exponentially loaded tapered perforated beams more efficiently than PINNs.
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Hybrid Edge-HPC Systems for Low-Latency Data-Driven Inference
RBF is a hybrid edge-HPC architecture that decouples low-latency edge inference using surrogate models from asynchronous HPC-driven model updates for simulation-bounded cyber-physical systems.