Search
10 articles for “neural network surrogate model”
-
Neural Network Surrogate Model for Fast Prediction of Sheet Resistance and Transmittance in Polymer-Coated Silver Nanowire Electrodes
Abstract: Hybrid transparent electrodes that combine silver nanowire (AgNW) networks with conductive PEDOT:PSS overcoats are promising replacements for indium tin oxide in flexible solar cells, but their design requires balancing sheet resistance (Rs) against transmittance (T). Physics-based percolation simulations capture this trade-off but are too slow for large design searches. We present a neural-network surrogate trained on 3,000 Latin-hypercube designs from a Monte Carlo resistor-network simulator in which the polymer acts …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
-
AI-Assisted Optimization of Supersonic Airfoil Shapes Using CFD Coupling
Abstract: This paper presents a novel framework for optimizing supersonic airfoil geometries through integrated artificial intelligence and computational fluid dynamics coupling. Traditional gradient-based optimization methods for high-speed aerodynamic shapes suffer from computational expense and convergence difficulties in non-convex design spaces. The proposed methodology employs a deep neural network surrogate model trained on high-fidelity Reynolds-Averaged Navier-Stokes solutions to approximate aerodynamic performance metrics across the design space. A hybrid particle swarm-genetic algorithm searches …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 8–17 Read article
-
Hopf Bifurcation–Driven Chiral Amplification in an Oregonator-Based Oscillatory Chemical System with Optimal Control of Time-Scale Separation
Abstract: Nonlinear chemical systems exhibiting oscillatory kinetics and symmetry-breaking behavior have been widely studied in the context of reaction–diffusion chemistry and crystallization processes. However, the interaction between oscillatory dynamics and chiral amplification mechanisms remains insufficiently understood within a unified dynamical systems framework. This work aims to develop and analyze a reduced-order nonlinear model that couples an Oregonator-type activator–inhibitor system with a chiral response variable, and to investigate the role of Hopf …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 3, 2026 Read article
-
Neuromorphic Spiking Neural Networks and Event-Driven Hardware: Architectures, Learning Paradigms, and Experimental Realizations
Abstract: With the current computational boom the research community is seeking for more sustainable energy efficient i.e. biologically inspired models of conventional Artificial Neural Networks (ANNs). Spiking Neural Networks (SNNs) known as the third generation of neural network models, provide a revolutionary approach by mimicking the asynchronized event-driven and temporally accurate signaling of the mammalian brain. Whereas conventional deep learning models operate with real-valued activations and dense matrix multiplications, SNNs use …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 39–46 Read article
-
AI-Accelerated Development of Gradient Polymer Nanocomposite Thin Films
Abstract: Gradient polymer nanocomposite thin films are an active field of materials research due to the fact that it enables scientists to de-facto regulate the optical, electrical, and mechanical properties of a film by merely altering its composition on a layer-by-layer basis. This type of control opens the gate to the improved flexible electronics, long lasting protective coats, and the new smart gadgets. The problem is, though, that it is a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 456–469 Read article
-
Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation
Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L), …
Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article
-
Machine Learning Optimization for VARTM Carbon Polymer Laminates
Abstract: Vacuum-assisted resin transfer moulding (VARTM) is a key low-cost, out-of-autoclave process for manufacturing large-scale carbon-fibre reinforced polymer (CFRP) laminates crucial to aerospace wings, wind-turbine blades, marine hulls, and automotive structures. Unpredictable resin flow often leads to voids, dry spots, and race-tracking defects, resulting in 27.9% scrap rates and lengthy, costly trial-and-error design cycles. Although surrogate models provide rapid impregnation predictions for simple flat-plate geometries, vision-based monitoring is limited to idealized …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 229–245 Read article
-
An Aggregate-Breakage Percolation Model and ANN-Based Internal Validation for Curvature-Dependent Electrical Conductivity of MWCNT/TPU Nanocomposites
Abstract: Strain-dependent percolation models are mostly built and tested for uniaxial tension, yet many flexible sensors and stretchable devices work mainly in bending, where the outer fibre is stretched, the inner fibre is compressed and the strain changes with thickness. This study extends an aggregate-breakage percolation model, originally formulated for uniaxial strain, to through-thickness bending of a multi-walled carbon nanotube/thermoplastic polyurethane (MWCNT/TPU) nanocomposite. The local strain is taken as ε(y) = …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
-
AI-Assisted Design of a Compact UWB Antenna with Tunable Band-Notch Characteristics
Abstract: Ultra-wideband antennas require wide impedance bandwidth while maintaining stable radiation characteristics and suppressing interference from coexisting narrowband wireless services. Conventional optimization of UWB antenna geometries generally requires repeated full-wave electromagnetic simulations, making parameter exploration computationally expensive. This paper proposes an artificial-intelligence-assisted methodology for designing a compact UWB monopole antenna with a tunable band-notch characteristic. The proposed antenna uses a microstrip-fed planar monopole printed on an FR-4 substrate, while a tunable …
Published in Journal of Microwave Engineering and Technologies · Vol. 13, Issue 2, 2026 Read article
-
Artificial Intelligence–Assisted Reduced-Order Modeling and Stability Control in Granular Couette Flow
Abstract: This study develops a reduced-order and stability-aware modeling framework for dense granular Couette flow by integrating continuum mechanics, bifurcation analysis, and data-driven stability estimation. Starting from coupled governing equations for momentum, granular temperature, and microstructural evolution, the system is nondimensionalized and reduced using a Galerkin projection consistent with shear-driven boundary conditions. This yields a low-dimensional nonlinear dynamical system that preserves the essential coupling between velocity, fluctuation energy, and structural relaxation. …
Published in Emerging Trends in Chemical Engineering · Vol. 13, Issue 3, 2026 Read article