Universal Differential Equations unify scientific models with machine learning by embedding flexible approximators into differential equations, enabling applications from biological mechanism discovery to high-dimensional optimization.
Evolutionary algorithms in genetic regulatory networks model
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abstract
Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.
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Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
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Universal Differential Equations for Scientific Machine Learning
Universal Differential Equations unify scientific models with machine learning by embedding flexible approximators into differential equations, enabling applications from biological mechanism discovery to high-dimensional optimization.
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Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.