The field of scientific machine learning is moving towards the development of innovative methods for solving high-dimensional problems. Researchers are exploring new architectures and techniques to improve the accuracy and efficiency of neural networks in solving complex equations and modeling dynamical systems. One of the key directions is the use of neural ordinary differential equations (Neural ODEs) and neural operators to solve partial differential equations (PDEs) and other high-dimensional problems. These methods have shown promising results in terms of accuracy and computational efficiency. Another area of focus is the development of new attention mechanisms, such as linear attention neural operators, which can reduce the computational cost of neural networks while preserving their expressive power. The use of adversarial training and active learning techniques is also being explored to improve the generalizability and robustness of neural networks. Notable papers in this area include: High order Tensor-Train-Based Schemes for High-Dimensional Mean Field Games, which introduces a novel scheme for solving high-dimensional Mean Field Games systems using Tensor-Train decompositions. Deep Neural ODE Operator Networks for PDEs, which proposes a deep neural ODE operator network framework for solving PDEs. Efficient High-Accuracy PDEs Solver with the Linear Attention Neural Operator, which presents a novel type of neural operator that achieves both scalability and high accuracy. Protein Folding with Neural Ordinary Differential Equations, which proposes a continuous-depth formulation of the Evoformer using Neural ODEs for protein structure prediction. Towards Universal Solvers: Using PGD Attack in Active Learning to Increase Generalizability of Neural Operators as Knowledge Distillation from Numerical PDE Solvers, which proposes an adversarial teacher-student distillation framework to improve the generalizability of neural operators.