Research Projects

Physics-structured machine learning — operators, conservation, verification, and honest measurement.
Sehaj Randhir Singh
Independent researcher; partial affiliation with NYU Tandon School of Engineering

Every project below follows the same discipline: an idea, stated so it can fail; a measurement that could kill it; a manuscript written from programmatic macros so no number is retyped; and a public artifact trail — committed per-seed result JSONs, one-click reruns, trained checkpoints on Hugging Face, result mirrors on Kaggle — so every claim can be traced and re-executed. The common thread is structure: conservation laws held exactly (Metriplectic), geometry carried through depth (AGF-NO), governing equations in attention (PhysFormer), stability guaranteed by proof (certified Lyapunov control), and benchmarks with independent verifiers (PhysBench). Where an idea failed, the failure is reported in the same typeface as the wins.

Selected projects

01
Metriplectic layers: a neural layer class with exact conservation laws for physical AI
02
EB-H-JEPA: Energy-based Hamiltonian JEPA world models — failure atlas on Lorenz-63 + controlled Crafter sample-efficiency test of physical priors in a DreamerV3-style agent
03
AGF-NO: restoring the truncated band — geometry-aware Fourier Neural Operators, and a direct measurement of geometric forgetting in 2-D and 3-D
04
PhysFormer — a transformer adjusted for physics. Law-conditioned attention feeds the governing equation in as input tokens (not only as loss); a ten-law pre-registered regime test was falsified and is reported honestly, with DeepONet external baselines. Every number traces to a committed JSON.
05
PhysBench — a verifiable 12-domain benchmark for physics-informed ML. Independent-verifier principle: targets generated separately from the training signal, predictions scored against governing-equation residuals written from scratch. Committed 75-run baseline matrix + DeepONet comparison. Every number traces to a committed JSON.
06
Physics supervision in the loss has two separable effects: it enforces consistency 19x (p<1e-7) — and it does not buy accuracy (p=0.65). 75-run controlled matrix, all committed. Site + paper + 49 physics tests.
07
Consistency is not accuracy at the field level. The showcase heat-plate number (5.9%) is the easiest member of its own distribution (~29% honest); enforcing the residual drops it 6-8x while fidelity degrades. DeepXDE tradeoff stated plainly. Site + paper + 49 physics tests.
08
Transfer across physics laws: 2.9x lower error than a specialist at 25% data. Measured decomposition — the vocabulary carries it 10x, the residual constrains it 7x. Per-seed, committed, re-runnable. Site + paper + 49 physics tests.
09
The missing control in agent evaluation is a gate, not a better model. Same agent, with and without verification: 0% vs 29% false success, all injected faults caught vs none — with the honest n=7 statistics stated. Site + paper + 16 node tests + 49 physics tests.
10
Trigger backdoors in LoRA-instruction-tuned LLMs: injection, persistence, detection, and removal — reproducible, CPU-friendly, with full artifacts.
11
Identity Through Interruption: a motion-signature-first decision architecture for multi-object Re-ID. Zero ID switches through every constructed synthetic interruption; pose-free cluster tracking cuts MOT17 IDSW 1,265 -> 995 (-21.3%). NMI draft + WACV 2027 + IEEE papers, JEPA-style project page, every number traced to a committed JSON.
12
Angle-Weighted Neural Networks: angles as an axis of compute — w = r·u, composition by addition (52× smaller compositional gap than a matched MLP), readable settled shapes, NMI + IEEE manuscripts, project site.

All project sites

•  Adaptive Contact Dynamics •  Age Artificial General Engineer •  The Cost of Automatic Action Labelling •  Verifying Neural Lyapunov Certificates •  Certified Training of Lyapunov Functions •  Compositional Grid Certificates •  The Dirty Man: Switch Operators •  Dreamerv3 Latent Stability •  Electric_Adaption •  Gnome •  Gnome Manufacturing •  Gnome Psn1 Nmi •  Morphogenetic Bimodal Networks •  Multi-Agent ISS Certificates •  Operating Atlas •  Operator •  Perch •  Honest Physics-ML Components •  PhysRNet •  The Rank-Fidelity Paradox •  DataFly: Robot Data Curation •  Self Modeling Stable Control •  Spectral–Topological Decoupling •  Steerable VLA •  Stochastic Latent Bounds •  Tactile Process Control •  The Tolerance Law •  Certifying the Residual Stream •  Ugct •  Verified Voltage Control •  Website