Digital twins are transforming engineering and applied sciences by enabling real-time monitoring, simulation, and predictive analysis of physical systems and processes. As outlined in the 2024 report by the National Academies of Engineering, Science, and Medicine [1], digital twins differ from both forward digital models and digital shadows [2]. A digital twin is a tailored virtual representation that captures key attributes of a physical system or process. This digital representation synchronizes with its physical counterpart by assimilating sensor data and refining predictive capabilities. Through this continuous updating, digital twins can simulate what-if scenarios, supporting predictive decision-making aimed at maximizing value. Conventional digital twins primarily rely on fixed computational models and passive data assimilation, which limit their adaptability in uncertain and dynamic environments. Both research and industry recognize the need for a new level of autonomy and resilience in digital twins by closing the loop between perception and action — fostering sentient digital twins equipped with learning and adaptation capabilities that actively seek information to improve situational awareness and manage the evolution of their environment. At the same time, new forms of computation can emerge within the physical counterpart itself, enabling embedded computational capabilities for processing external stimuli or assimilating sensor data. This session aims to gather contributions highlighting the impact of self-learning, adaptation, and information seeking strategies on the ability of digital twins to react to uncertain and dynamic environments. Contributors are invited to discuss topics including (but not limited to) self-learning and self-adaptivity in smart structures; uncertainty quantification, propagation, and resolution in digital twins; parameter and state estimation for decision support; computationally efficient signal interpretation; hybrid physics-data approaches; multi-agent control of physical systems; and (physical) reservoir computing. [1] National Academy of Engineering and National Academies of Sciences, Engineering, and Medicine, Foundational Research Gaps and Future Directions for Digital Twins, 2024. [2] W. Kritzinger, M. Karner, G. Traar, J. Henjes, W. Sihn, Digital Twin in manufacturing: A categorical literature review and classification, 2018.