ib物理hl难吗?ib物理考点避坑指南

本文是一位IB大考毕业生分享的物理HL备考经验,涵盖测量与误差、动力学、热力学等多个章节的重点与注意事项。文章强调物理HL需要深入理解,指出Paper1和Paper2的不同侧重点,并提供了各章节的复习建议和易错点提示。

我在去年参加了IB大考,目前就读于维克森林大学。之前已经为大家分享了经济HL和数学MAA HL的备考经验,今天继续给大家分享一些物理HL的备考经验。ib物理hl难吗

我认为物理算是在理科中偏理解的一门学科了,对于知识的理解程度要求很高, 并且一个理解差错就会导致整道题都错光,代价非常惨痛(流泪)。

所以我希望能通过分享一些注意要点帮助大家考一个更高分。我在去年考的IB大考,Paper3由于疫情原因被取消了,所以相关的考点我没法提供太多建议,但是很多点我相信都是互通的。下面我就根据不同的章节来分享需要注意的知识点与考点。

简要

考试主要分为Paper1选择题和Paper2计算与简答题,两个Paper所涉及的考点也较为好辨认,Paper1更偏向于基础的只是概念和较为简单的计算题,而Paper2涉及的是比较复杂的计算题和解释题,当然也有一部分知识点可能会同时出现在两个Paper上。

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虽然对考试用处并不大,但是复习时也可以根据知识点的大小进行Paper的预测,帮助更有效的复习!

Chapter 1: Measurementsand errors

Chapter 1的内容是最基础也是最简单的,考分都比较低,考得都不会太难。考得比较多的部分是uncertainty,要求你计算误差的大小:比较需要注意的是找整个算式的大框架是由加减组成还是以乘除组成,然后应用对应的误差公式。

在error方面要分清楚systematicerror和random error的区别,和不同减少误差的方法。另外我比较推荐的是记一些比较常用的数字量级(10的负几次方),如地球质量,原子质量与体积等。虽然很多在booklet上有,但是记一下当然可以更快方便答题啦。

Scalar and vector部分主要要了解的是各个vari

### Wasserstein Distribution Robustness in Machine Learning In the context of machine learning, distributional robustness aims to ensure that models perform well not only on training data but also under various perturbations or shifts in the underlying data distribution. The concept of Wasserstein distribution robustness specifically leverages the Wasserstein distance (also known as Earth Mover's Distance) between probability distributions. The Wasserstein distance measures how much "work" is required to transform one probability distribution into another by moving mass from one location to another with a cost proportional to the distance moved. In terms of model optimization and evaluation, this translates into constructing algorithms that are optimized over an ambiguity set defined around the empirical distribution using the Wasserstein metric[^2]. For instance, when training a classifier, instead of minimizing loss solely based on observed samples, one can minimize the worst-case expected risk within a small neighborhood of possible distributions centered at the empirical distribution according to the Wasserstein distance. This approach helps mitigate risks associated with potential covariate shift where test-time inputs may come from slightly different distributions than those seen during training. Moreover, incorporating Wasserstein-based regularization techniques has been shown effective against adversarial attacks because it encourages smoother decision boundaries which reduce vulnerability to carefully crafted input modifications designed to fool classifiers while being imperceptible to humans. Additionally, applying such principles involves formulating problems mathematically so they remain computationally tractable despite involving complex integrals related to transport costs across high-dimensional spaces. Techniques like entropic smoothing facilitate efficient computation through convex duality theory allowing practical implementation even for large-scale datasets encountered today. ```python import torch from torch import nn from torchvision.models import resnet18 class WassersteinRobustModel(nn.Module): def __init__(self, base_model=resnet18(pretrained=True)): super(WassersteinRobustModel, self).__init__() self.base_model = base_model def forward(self, x): return self.base_model(x) # Note: Actual implementation would require defining specific components necessary for enforcing Wasserstein constraints. ```
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