  The objective of personalized medicine is to tailor interventions to an
individual patient's unique characteristics. A key technology for this purpose
involves medical digital twins, computational models of human biology that can
be personalized and dynamically updated to incorporate patient-specific data
collected over time. Certain aspects of human biology, such as the immune
system, are not easily captured with physics-based models, such as differential
equations. Instead, they are often multi-scale, stochastic, and hybrid. This
poses a challenge to existing model-based control and optimization approaches
that cannot be readily applied to such models. Recent advances in automatic
differentiation and neural-network control methods hold promise in addressing
complex control problems. However, the application of these approaches to
biomedical systems is still in its early stages. This work introduces
dynamics-informed neural-network controllers as an alternative approach to
control of medical digital twins. As a first use case for this method, the
focus is on agent-based models, a versatile and increasingly common modeling
platform in biomedicine. The effectiveness of the proposed neural-network
control method is illustrated and benchmarked against other methods with two
widely-used agent-based model types. The relevance of the method introduced
here extends beyond medical digital twins to other complex dynamical systems.
