Identifying cost-effective indigenous building materials that minimise heat penetration through walls is critical for indoor thermal comfort in low-income rural housing in hot-dry climates, where summer temperatures routinely exceed 45 C. We present a two-stage computational framework for thermal ranking of five low-cost indigenous wall materials: mud brick, clay-straw adobe, lime-stabilised bamboo panel, fired clay brick, and lime-mud composite. First, a validated Crank-Nicolson finite difference method (FDM) solves the one-dimensional transient heat equation with Robin boundary conditions under diurnal solar and outdoor air-temperature forcing, generating 1500 periodic-day solutions across a nine-dimensional parameter space by Latin Hypercube sampling. Second, a Physics-Informed Neural Operator (PINO) with a Fourier Neural Operator (FNO) backbone learns the parameter-to-solution operator mu ->T(x,t), enforcing both data fidelity and PDE consistency. The trained PINO attains a relative L2 field error of 5.14e-4 and a 0.201 K mean absolute error on the peak inner surface temperature, preserving the FDM material ranking exactly; PINO trained on 150 FDM samples matches a data-only FNO trained on twice as many, so the physics loss is most valuable when data are scarce. The periodic-day formulation also yields the ISO 13786 time lag and decrement factor, reproduced to within 0.99 h and 0.010. At nominal hot-dry summer conditions, clay-straw adobe achieves the best cost-performance index among widely available materials. A climate sweep, confirmed by FDM spot checks, reveals a regime boundary: under sub-ambient outdoor conditions the ranking inverts to conductive fired clay brick, delineating heat-exclusion and heat-rejection regimes. The framework supports evidence-based material selection for post-flood reconstruction in hot-dry regions.
Muhammad Akbar Khan, Fahim Raees, Ubaida Fatima· arXiv.org· 0 citations
This study presents a machine learning framework for predicting the Young's Modulus (YM) of biomedical titanium alloys to address stress shielding in implant applications. A Deep Neural Network (DNN) was developed using nineteen compositional features and physically meaningful descriptors. Prior to model training, the dataset was preprocessed using Box-Cox and Yeo-Johnson transformations to improve data distribution while preserving all samples. The model architecture incorporates multiple hidden layers with L1/L2 regularization and dropout to enhance generalization. Training was conducted using early stopping, terminating at 585 epochs to prevent overfitting. The optimized model achieved a testing Mean Squared Error (MSE) of 0.294 Gpa and r2-score of 0.82, demonstrating predictive performance. Comparative analysis with XGBoost, Random Forest, Gradient Boosting, and Support Vector Machine confirmed the capability of the proposed DNN model for capturing complex behavior non-linear relationships in Ti-Alloys data.
Muhammad Shahmir Saif, Muhammad Ali Siddiqui, Fahim Raees· Scientific Reports· 0 citations
We present a systematic ablation study of physics-informed neural networks (PINNs) for level-set advection across four benchmarks of increasing complexity: linear translation (TR), solid-body rotation (RO), reversed vortex deformation (RV), and the Zalesak rotating slotted disc (ZD), covering 69 experiments. For TR, a step learning rate scheduler (StepLR) with eikonal weight weik=1.0 is optimal (mean L2 error E―L2=2.09×10−4). For RO, cosine annealing (CosineAnnealing, minimum learning rate ηmin=10−5) outperforms StepLR, establishing that scheduler choice is benchmark-specific and cannot be transferred. For RV, reducing weik from 1.0 to 10−4 yields an 82× improvement ( E―L2=1.51×10−3, mean relative L2 error E―L2rel=0.43%, final time T=2); extending to T=8 with causal weighting and residual-based adaptive distribution and refinement (RAD + RAR) achieves 0.63% with a standard tanh network, outperforming the PirateNet state-of-the-art (Sota) of Mullins et al (2025) ( 0.85%). For ZD, four studies are conducted: the eikonal weight study (S1), the progressive sampling study (S2), the architecture study (S3), and the adaptive sampling study (S4). Random Fourier feature (RFF) encoding (bandwidth σ=2) with weik=10−2 and causal weighting achieves E―L2=5.74×10−4 in the architecture study (S3), a 10× reduction over the tanh baseline; adding RAD + RAR with RFF σ=5 and M=32 causal chunks achieves the best overall ZD result: E―L2=4.64×10−4 ( E―L2rel=0.13%) in 15.2 min in the adaptive sampling study (S4), outperforming the published Sota. A key finding is an RFF–eikonal joint design constraint: at low bandwidth ( σ=2), weak eikonal regularization ( weik=10−4) distorts the signed-distance field and underperforms the tanh baseline, whereas higher bandwidth ( σ=5) is compatible with moderate regularization ( weik=10−3) and yields the global best result. To the best of our knowledge, this bandwidth-dependent constraint has not previously been identified in the PINN literature.
Muhammad Akbar Khan, Fahim Raees· Machine Learning: Science an...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
