Universiti Putra Malaysia Institutional Repository
metadata only
An optimization-driven physics-informed neural network for variable-order fractal–fractional differential equations: application to the bloch model
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Lazy training of quantum physics informed neural networks#1013837
- INDEQS: Informed Neural controlled Differential EQuationS#287145
- Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving#299987
- Dynamic Spacetime Fluid Theory (FED 2.5): A Consistent Framework for Fractal Density Networks and Galactic Rotation Curves. Consolidated and Verified Version.#118962
- Gradient Fractals - The Fractal Veldt - Essay IX: The First Depth - The Gradient Fractal Field at Grain δ₁ = 5/2: The Unconstrained Initial Depth, the Necessary Initial Condition, and the First Physical Instantiation#716631
Record · ID 425963
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive