JuliaCon 2023

DyVE, a Framework for Value Dynamics
07-26, 12:00–12:30 (US/Eastern), 32-D463 (Star)

DyVE (Dynamics of Value Evolution) is an open source framework, aimed at designing, fitting, and integrating complex real-world process models; accounting for the accrual of costs and rewards; and supporting complex decision making, particularly in pharmaceutical R&D and business.

Process specifications are compact and algebraically composable. Uncertainty about model values or structure is supported. DyVE is interoperable with Scientific Machine Learning (SciML).


DyVE (Dynamics of Value Evolution) is aimed at designing, fitting, and integrating dynamical models of real-world processes; accounting for the accrual of costs and rewards; and supporting complex decision making, particularly in pharmaceutical R&D and business.

Process and resource models are specified in a compact DSL inspired by Applied Category Theory (ACT). Process specifications are algebraically composable. Uncertainty about model values, as well as structure, can be represented by distributions or by more general user-defined probabilistic expressions. The DSL is essentially a bridge from ACT semantics to Scientific Machine Learning (SciML).

DyVE can also co-integrate its models with pre-existing non-DyVE models, supporting the APIs of DifferentialEquations.jl, Agents.jl, or AlgebraicJulia.jl.

Processes are represented as hybrid state-rewriting systems. Several rewriting formalisms are supported: discrete maps, differential equations, discrete events, agent-based models, and combinations thereof.

Simulation, fitting, optimization, visualization, and other computational heavy lifting is delegated to the SciML ecosystem.

We will present and demonstrate component packages of DyVE on illustrative applications.

J B

Research Scientist at Merck & Co.

Mathematical epidemiologist, senior scientist at Merck.