## dealing with small datasets:

- normalizing the inputs
- LOOCV/LPOCV [link](https://pmc.ncbi.nlm.nih.gov/articles/PMC4454870/)
- **Interpolation & Function Approximation**:
	- If your data follows a certain structure, use polynomial interpolation (Lagrange, Chebyshev), splines, or radial basis functions (RBF) to generate synthetic data in between known points.
- **Manifold Learning & Dimensionality Reduction**: .[link](https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-040522-115238?crawler=true)
	- If your input data lies on a lower-dimensional manifold, use techniques like PCA, diffusion maps, or autoencoders (math-based, not deep-learning) to reduce the complexity of the problem.
	
- **Differential Equations & Functional Analysis**:

	- If your problem is governed by differential equations (ODEs, PDEs), use numerical solvers to generate additional synthetic data.
- **another interpolation thingy** [link](https://www.researchgate.net/publication/389581352_Interpolation_Neural_Network-Tensor_Decomposition_INN-TD_a_scalable_and_interpretable_approach_for_large-scale_physics-based_problems/link/67c87202d75970006505e664/download?_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6InB1YmxpY2F0aW9uIiwicGFnZSI6InB1YmxpY2F0aW9uIn19) 