Knowledge-Inclusive Adaptive Physics-Informed Neural Network for Microbial Interaction Modelling
arXiv:2606.07686v1 Announce Type: new Abstract: Physics-Informed Neural Network (PINN) is a way of including knowledge in the form of equations in Machine Learning methods. Beyond equations, knowledge exists in other forms, such as text and network structure. While existing PINN-based approaches discover equation parameters from data, they rely solely on experimental measurements. We propose a new PINN framework that enriches parameter discovery by incorporating auxiliary knowledge sources. We instantiate our framework for microbiology, where generalised Lotka-Volterra (gLV) serves as a biolog
The increasing sophistication of AI models and the demand for more robust, knowledge-infused machine learning drive advances in hybrid AI approaches like PINNs.
This development moves beyond purely data-driven AI, integrating existing scientific knowledge to create more accurate and interpretable models, particularly in complex domains like biology.
Machine learning models, particularly in scientific discovery, can now leverage diverse forms of knowledge (text, networks) alongside experimental data, enhancing their predictive power and reducing reliance on vast datasets alone.
- · Synthetic biology researchers
- · Pharmaceutical industry
- · AI model developers
- · Biotechnology sector
- · Purely data-driven black-box AI approaches
More accurate and efficient modeling of microbial interactions for drug discovery and environmental science.
Accelerated development of synthetic biology applications through improved predictive modeling of biological systems.
Potential for AI-driven discovery of novel biological mechanisms and interventions with reduced experimental costs.
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Read at arXiv cs.LG