Deep Material Network (DMN) is a data-driven surrogate model of heterogeneous materials which is built by combining analytical homogenisation solutions and material constitutive relations into a neural network model, yielding mechanistic building blocks [1]. DMN was reformulated from the interaction view-point as the Interaction-Based DMN (IB-DMN) [2], in order to improve its training performance. In this work, the phase volume fraction of composite materials is decoupled from the topological parameters of the IB-DMN so that the phase volume fraction is no longer influenced by the topological parameters. This allows constructing a stochastic IB-DMN by introducing uncertainties to the topological parameters of a general IB-DMN. The nonlinear predictions of the proposed stochastic IB-DMN are compared to those from Direct Numerical Simulation (DNS) on 2D Stochastic Volume Elements (SVEs) of unidirectional fiber-reinforced matrix composites under finite strain. Besides, damage is introduced in the matrix constituent phase of the unidirectional fiber-reinforced matrix composites. The handling of damage in general, and of softening in particular, by the IB-DMN is therefore investigated. This project has received funding from the European Union’s Horizon Europe Framework Programme under grant agreement No. 101056682 for the project ‘‘DIgital DEsign strategies to certify and mAnufacture Robust cOmposite sTructures (DIDEAROT)’’. The contents of this publication are the sole responsibility of ULiege and do not necessarily reflect the opinion of the European Union. Neither the European Union nor the granting authority can be held responsible for them. REFERENCES [1] Z. Liu, C. Wu, M. Koishi, “A deep material network for multiscale topology learning and accelerated nonlinear modelling of heterogeneous materials”, Comput. Meth. in Appl. Mech. and Engng. Vol. 345, pp. 1138–1168, (2019) [2] V. D. Nguyen, L. Noels, “Interaction-based material network: A general framework for (porous) microstructured materials”, Comput. Meth. in Appl. Mech. and Engng. Vol. 389, pp. 114300, (2022) [3] L. Wu,, L. Noels, “Stochastic Deep Material Networks as Efficient Surrogates for Stochastic Homogenization of Nonlinear Heterogeneous Materials” In preparation