High-resolution digital elevation models (DEMs) are essential for accurate vehicle motion simulation, yet are costly to obtain and store. We propose a DEM super-resolution method based on a coordinate-based multilayer perceptron (MLP), which represents elevation as a continuous function over spatial coordinates. A hypernetwork predicts the parameters of the MLP from low-resolution inputs, enabling direct querying of elevation and slope values at arbitrary locations via automatic differentiation. To enhance terrain detail reconstruction, we incorporate periodic activation functions and terrain-aware loss functions, including slope and frequency-domain components. Experimental results show that the proposed method outperforms bicubic interpolation in both elevation and slope prediction, while reducing memory usage to just 14.9% of explicit DEM storage. Furthermore, it enables efficient end-to-end generation of a parameterized MLP. We further demonstrate its integration into a vehicle simulation system, where the continuous and differentiable terrain model significantly improves simulation realism. The proposed method is compact, accurate, and easily deployable in real-time terrain-aware applications.