Loss functions¶

Selecting a loss¶

The training loss is a Hydra config group. The default is set in icenet_mp/config/base.yaml (loss: huber) and can be overridden on any command line:

imp train --config-name <config> loss=mse
imp train --config-name <config> loss=amse loss.mode=hybrid loss.spectral_weight=0.1

Each option corresponds to a file in icenet_mp/config/loss/, whose header comments carry the full parameter documentation; this page summarises how to choose between them.

Supported losses¶

`loss=`	what it is	pros	cons
`huber` (default)	quadratic below `delta`, linear above	smooth near zero, robust to outliers, loss stays in target units above `delta`	needs `delta` chosen; below `delta` it inherits MSE's blur incentive (see AMSE)
`mse`	squared residual	smooth everywhere; strong gradient on large errors	outlier-sensitive; strongest amplitude-damping (blur) incentive
`mae`	absolute residual	fully outlier-robust; constant gradient	non-smooth at zero; weak signal on small errors
`rmse`	`sqrt(MSE + eps)`	interpretable in target units	same optimum as MSE; gradient rescaled by the running loss value
`smooth_l1`	Huber variant with `beta` transition	as Huber; PyTorch-native parameterisation	as Huber
`weighted_mse` / `weighted_l1` / `weighted_bce`	elementwise `sample_weights` × MSE / L1 / BCE-with-logits	spatial weighting (e.g. emphasise the ice edge or active cells)	weights must be supplied and justified; BCE assumes a [0, 1] classification framing
`amse`	spectral anti-blur loss (Subich et al. 2025, arXiv:2501.19374, flat-grid adaptation)	removes the "double penalty": matching the target's power spectrum per scale band is optimal at any coherence, so partially-predictable fine scales are no longer rewarded for being damped	more expensive than pointwise losses (FFT per step); `hybrid` mode introduces `spectral_weight` to calibrate

Why an anti-blur loss exists (the double penalty, in two sentences)¶

For any squared-error-type loss, the loss-optimal amplitude of a spatial scale the model cannot predict perfectly equals its coherence with the target — at 50 % coherence the optimal move is to halve that scale's amplitude. Spatial blur is therefore the optimum of pointwise training, not a failure of it; AMSE modifies the per-scale decomposition so that preserving the target's spectrum is optimal instead (full derivation in the header of icenet_mp/losses/amse_loss.py).

AMSE options¶

loss.mode=hybrid (default): standard Huber plus spectral_weight × the AMSE excess. At spectral_weight=0 this is exactly the Huber control. Calibrate spectral_weight so the spectral term carries ~10 % of the total loss at initialisation (the untrained model's Huber/excess ratio differs per architecture).
loss.mode=pure: the AMSE objective alone (no spectral_weight).
loss.wavenumber_weight=fastnet (default off): re-weights the spectral penalty toward fine scales by max(N_k * k^sqrt(3), 1) (FastNet, arXiv:2509.17601) at unchanged total, moving the penalty onto ice-edge scales.
loss.static_ref_path=<path.npy> (default off): subtracts a static reference field from both prediction and target before the spectral terms, so static climatological structure does not dilute the anti-blur signal.

Both optional flags are bit-for-bit inert when off.