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Non-Negative Universal Differential Equations With Applications in Systems Biology
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Universal differential equations (UDEs) leverage the respective advantages of mechanistic models and artificial neural networks and combine them into one dynamic model. However, these hybrid models can suffer from unrealistic solutions, such as negative values for biochemical quantities. We present non-negative UDE (nUDEs), a constrained UDE variant that guarantees non-negative values. Furthermore, we explore regularisation techniques to improve generalisation and interpretability of UDEs.
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Cited by 1 Pith paper
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Experimental Design for Missing Physics
A sequential experimental design technique discriminates between model structures from symbolic regression to discover missing physics in process systems such as bioreactors.
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