Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
Machine learning for computational science and engineering, Journal Year: 2025, Volume and Issue: 1(1)
Published: March 11, 2025
Language: Английский
Citations
10Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 116728 - 116728
Published: Jan. 1, 2025
Language: Английский
Citations
3Batteries, Journal Year: 2025, Volume and Issue: 11(3), P. 107 - 107
Published: March 13, 2025
Artificial Neural Networks (ANNs) improve battery management in electric vehicles (EVs) by enhancing the safety, durability, and reliability of electrochemical batteries, particularly through improvements State Charge (SOC) estimation. EV batteries operate under demanding conditions, which can affect performance and, extreme cases, lead to critical failures such as thermal runaway—an exothermic chain reaction that may result overheating, fires, even explosions. Addressing these risks requires advanced diagnostic strategies, machine learning presents a powerful solution due its ability adapt across multiple facets management. The versatility ML enables application material discovery, model development, quality control, real-time monitoring, charge optimization, fault detection, positioning it an essential technology for modern systems. Specifically, ANN models excel at detecting subtle, complex patterns reflect health performance, crucial accurate SOC effectiveness applications this domain, however, is highly dependent on selection datasets, relevant features, suitable algorithms. Advanced techniques active are being explored enhance improving models’ responsiveness diverse nuanced behavior. This compact survey consolidates recent advances estimation, analyzing current state field highlighting challenges opportunities remain. By structuring insights from extensive literature, paper aims establish ANNs foundational tool next-generation systems, ultimately supporting safer more efficient EVs robust safety protocols. Future research directions include refining dataset quality, optimizing algorithm selection, precision, thereby broadening ANNs’ role ensuring reliable vehicles.
Language: Английский
Citations
3Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(2)
Published: Jan. 23, 2025
Language: Английский
Citations
1World Electric Vehicle Journal, Journal Year: 2024, Volume and Issue: 15(10), P. 460 - 460
Published: Oct. 10, 2024
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Traditional physical models often face challenges computational inefficiency and insufficient control precision when dealing with systems. However, by integrating laws directly into training process neural networks, PINNs can effectively learn capture kinematic characteristics vehicles, replacing traditional ordinary differential equation thus significantly efficiency performance. During model-training phase, this study further incorporates Theory Functional Connections (TFC) adaptive loss balancing strategies to efficiently solve ODE problems without relying numerical integration optimize strategy. combined approach not only reduces complexity, but also improves robustness strategy varying Numerical simulations demonstrate that offers significant advantages AGV trajectory-tracking tasks, manifested higher precise The proposal PINN-MPC provides new theoretical support innovative methods for real-time system control, important research application potential, is expected play key role future intelligent
Language: Английский
Citations
4Materials, Journal Year: 2025, Volume and Issue: 18(1), P. 191 - 191
Published: Jan. 5, 2025
Surface wettability, defined by the contact angle, describes ability of a liquid to spread over, absorb or adhere solid surface. wetting analysis is important in many applications, such as lubrication, heat transfer, painting and wherever liquids interact with surfaces. The behavior on surfaces depends mainly texture chemical properties Therefore, these studies show possibility modeling surface wettability adjusting parameters texturing process. prediction angle describing was performed using artificial neural networks. In order select most effective model, activation functions neurons, number hidden layers network training algorithms were changed. model presented capable predicting an efficiency coefficient determination R2 between real predicted angles over 0.9.
Language: Английский
Citations
0Published: Jan. 10, 2025
The use of neural networks for solving differential equations is practically difficult due to the exponentially increasing runtime autodifferentiation when computing high-order derivatives. We propose \(n\)-TANGENTPROP , natural extension TANGENTPROP formalism[1] arbitrarily many computes exact derivative \({d^{n}/d}x^{n}f{(x)}\) in quasilinear, instead exponential time, a densely connected, feed-forward network \(f\) with smooth, parameter-free activation function. validate our algorithm empirically across range depths, widths, and number demonstrate that method particularly beneficial context physics-informed where allows significantly faster training times than previous methods has favorable scaling respect both model size loss-function complexity as measured by required code this paper can be found at https://github.com/kyrochi/n_tangentprop. [https://github.com/kyrochi/n_tangentprop]
Language: Английский
Citations
0Ocean Engineering, Journal Year: 2025, Volume and Issue: 326, P. 120806 - 120806
Published: March 10, 2025
Language: Английский
Citations
0GeoInformatica, Journal Year: 2025, Volume and Issue: unknown
Published: March 19, 2025
Language: Английский
Citations
0Applied Mathematical Modelling, Journal Year: 2025, Volume and Issue: unknown, P. 116110 - 116110
Published: March 1, 2025
Language: Английский
Citations
0