(1/12): Digital Twins: National Academy Study on Digital Twins https://www.nationalacademies.org/digital-twins

(2/12): M2dt organizational chart

(3/12): RT: Derivative-informed neural operators (DINOs) to accelerate PDE-constrained optimization under high-dimensional uncertainty (UT)

(4/12): RT: Enabling high-dimensional inference using transport maps and dimension reduction (MIT; Poster 25, S2)

(5/12): RT: Point spread function (PSF) ice sheet Hessian approximation (UT & ANL)

(6/12): RT1.1, 1.2, 2.1: Digital twins for block copolymer model problem (BNL)

(7/12): RT1.1, 2.1, 2.3: Scalable Bayesian inference with measure transport and derivative-informed neural operators (MIT and UT)

(8/12): RT1.3: Flightpath planning for digital twin optimal experimental design (UT & ANL)

(9/12): RT2, RT3.3: Multi-fidelity uncertainty quantification (UQ) for ice-sheet simulations (UT)

(10/12): RT2.1, 3.1, 3.3: Nonlinear dimensionality reduction and ROMs for multiphysics, multiscale problems (UT & ORNL)

(11/12): RT2.2: Structure-preserving reduced order models (SP-ROMs) (SNL)

(12/12): RT3.1: Coupled heterogeneous methods (SNL)