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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Related Experiment Video

Updated: Jan 12, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy
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Multiprecision computing for multistage fractional physics-informed neural networks.

Na Xue1, Minghua Chen1

  • 1School of Mathematics and Statistics, Gansu Key Laboratory of Applied Mathematics and Complex Systems, Lanzhou University, Lanzhou 730000, People's Republic of China.

Chaos (Woodbury, N.Y.)
|November 4, 2025
PubMed
Summary
This summary is machine-generated.

Multistage fractional physics-informed neural networks (fPINNs) achieve high-precision solutions for subdiffusion equations, improving accuracy to 10-7∼10-8. This method overcomes challenges posed by nonlocal operators in traditional fPINNs.

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Area of Science:

  • Computational Mathematics
  • Numerical Analysis
  • Scientific Computing

Background:

  • Fractional physics-informed neural networks (fPINNs) were introduced by Pang et al. (2019) for subdiffusion equations, achieving relative errors of 10-3∼10-4.
  • High-precision numerical solutions for subdiffusion models are challenging due to the limited regularity caused by nonlocal operators.

Purpose of the Study:

  • To develop an advanced numerical method for high-precision solutions of subdiffusion equations.
  • To address the limitations of existing fPINNs in handling the low regularity of subdiffusion models.

Main Methods:

  • Introduction of multistage fractional physics-informed neural networks (fPINNs).
  • Building upon the framework of traditional multistage PINNs (Wang & Lai, 2024).
  • Implementation and testing on uniform and nonuniform computational meshes.

Main Results:

  • Achieved significant improvement in relative errors, reaching 10-7∼10-8 for subdiffusion equations.
  • Demonstrated the efficacy of multistage fPINNs in overcoming precision challenges.
  • Validated the method's performance on both uniform and nonuniform meshes.

Conclusions:

  • Multistage fPINNs provide a robust and accurate approach for solving subdiffusion equations.
  • The proposed method enhances numerical precision compared to previous fPINN techniques.
  • This advancement offers a more reliable tool for simulating complex fractional dynamics.